{"id":1080,"date":"2026-05-28T08:41:03","date_gmt":"2026-05-28T00:41:03","guid":{"rendered":"https:\/\/www.eutaboo.com\/index.php\/2026\/05\/28\/2026-05-28-%e5%8c%bb%e5%ad%a6%e5%9b%be%e5%83%8f%e5%88%86%e5%89%b2%e8%ae%ba%e6%96%87%e7%b2%be%e8%af%bb%ef%bc%9a3d-mri-%e8%92%b8%e9%a6%8f%e4%b8%8e%e5%8d%8a%e7%9b%91%e7%9d%a3%e5%8f%af%e9%9d%a0%e6%80%a7\/"},"modified":"2026-05-28T08:41:03","modified_gmt":"2026-05-28T00:41:03","slug":"2026-05-28-%e5%8c%bb%e5%ad%a6%e5%9b%be%e5%83%8f%e5%88%86%e5%89%b2%e8%ae%ba%e6%96%87%e7%b2%be%e8%af%bb%ef%bc%9a3d-mri-%e8%92%b8%e9%a6%8f%e4%b8%8e%e5%8d%8a%e7%9b%91%e7%9d%a3%e5%8f%af%e9%9d%a0%e6%80%a7","status":"publish","type":"post","link":"https:\/\/www.eutaboo.com\/index.php\/2026\/05\/28\/2026-05-28-%e5%8c%bb%e5%ad%a6%e5%9b%be%e5%83%8f%e5%88%86%e5%89%b2%e8%ae%ba%e6%96%87%e7%b2%be%e8%af%bb%ef%bc%9a3d-mri-%e8%92%b8%e9%a6%8f%e4%b8%8e%e5%8d%8a%e7%9b%91%e7%9d%a3%e5%8f%af%e9%9d%a0%e6%80%a7\/","title":{"rendered":"2026-05-28 \u533b\u5b66\u56fe\u50cf\u5206\u5272\u8bba\u6587\u7cbe\u8bfb\uff1a3D MRI \u84b8\u998f\u4e0e\u534a\u76d1\u7763\u53ef\u9760\u6027"},"content":{"rendered":"<h1>\u4eca\u65e5\u533b\u5b66\u56fe\u50cf\u5206\u5272\u6700\u65b0\u8bba\u6587\u7cbe\u8bfb\u8ffd\u8e2a<\/h1>\n<h2>\u4eca\u65e5\u7ed3\u8bba<\/h2>\n<p>\u4eca\u5929\u7b5b\u5230\u7684\u4e24\u7bc7\u6700\u503c\u5f97\u5173\u6ce8\u8bba\u6587\u90fd\u6765\u81ea 2026 \u5e74 5 \u6708\u4e0b\u65ec arXiv\uff1a\u4e00\u7bc7\u662f MICCAI 2026 \u63a5\u6536\u7684 3D MRI \u5206\u5272\u84b8\u998f\/\u538b\u7f29\u65b9\u6cd5\uff0c\u53e6\u4e00\u7bc7\u662f ICML 2026 \u63a5\u6536\u7684\u534a\u76d1\u7763 3D \u533b\u5b66\u5206\u5272\u53ef\u9760\u6027\u4e0e\u8bc4\u4f30\u534f\u8bae\u8bba\u6587\u3002\u6574\u4f53\u8d8b\u52bf\u662f\uff1a\u65b0\u5de5\u4f5c\u4e0d\u518d\u53ea\u5806 backbone\uff0c\u800c\u662f\u66f4\u5173\u6ce8\u5c0f\u6a21\u578b\u90e8\u7f72\u3001\u4f2a\u6807\u7b7e\u53ef\u9760\u6027\u3001multi-run \u8bc4\u4f30\u548c checkpoint \u62a5\u544a\u53ef\u4fe1\u5ea6\u3002<\/p>\n<h2>\u68c0\u7d22\u8bf4\u660e<\/h2>\n<p>\u4eca\u5929\u68c0\u7d22\u4e86 arXiv \u6700\u65b0\u63d0\u4ea4\uff0c\u5e76\u56f4\u7ed5 medical image segmentation\u30013D medical image segmentation\u3001polyp segmentation\u3001Mamba\u3001nnU-Net\u3001SAM\/MedSAM \u7b49\u5173\u952e\u8bcd\u7b5b\u9009\uff0c\u540c\u65f6\u68c0\u67e5\u4e86\u672c\u4efb\u52a1 2026-04-29 \u81f3 2026-05-27 \u7684\u5386\u53f2\u8f93\u51fa\u3002\u5f53\u5929\u6ca1\u6709\u53d1\u73b0\u6bd4 5 \u6708 25\u201326 \u65e5\u66f4\u65b0\u4e14\u540c\u65f6\u6ee1\u8db3\u201c\u65b9\u6cd5\u521b\u65b0\u660e\u786e\u3001\u5b9e\u9a8c\u5145\u5206\u3001\u4e0e\u533b\u5b66\u56fe\u50cf\u5206\u5272\u4e3b\u6d41\u6846\u67b6\u5f3a\u76f8\u5173\u201d\u7684\u4e24\u7bc7\u9876\u4f1a\/\u9876\u520a\u6b63\u5f0f\u8bba\u6587\uff0c\u56e0\u6b64\u5411\u524d\u56de\u6eaf\u9009\u62e9\u4e86\u4e24\u7bc7 2026 \u5e74\u8bba\u6587\uff1b\u6240\u6709\u5165\u9009\u8bba\u6587\u5747\u4e3a 2025 \u5e74\u53ca\u4ee5\u540e\u3002\u5df2\u68c0\u67e5\u5386\u53f2\u63a8\u8350\u8bb0\u5f55\u5e76\u6392\u9664\u4e86\u91cd\u590d\u8bba\u6587\uff1b\u4eca\u65e5\u8df3\u8fc7\u7684\u5386\u53f2\u5019\u9009\u5305\u62ec Patch-MoE Mamba\u3001MedCRP-CL\u3001Beyond Euclidean Prototypes \u7b49\u3002<\/p>\n<h2>WordPress \u53d1\u5e03<\/h2>\n<ul>\n<li>WordPress \u6587\u7ae0\u94fe\u63a5\uff1a\u5f85\u53d1\u5e03\u540e\u586b\u5199<\/li>\n<li>WordPress Post ID\uff1a\u5f85\u53d1\u5e03\u540e\u586b\u5199<\/li>\n<\/ul>\n<hr \/>\n<h2>\u8bba\u6587 1\uff1aDetail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation<\/h2>\n<h3>\u57fa\u672c\u4fe1\u606f<\/h3>\n<ul>\n<li>\u6807\u9898\uff1aDetail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation<\/li>\n<li>\u4f5c\u8005 \/ \u7b2c\u4e00\u4f5c\u8005\uff1aMengchen Fan, Baocheng Geng, Xi Xiao, Tianyang Wang, Siyuan Mei, Pulin Che, Xiaoqian Jiang, Qizhen Lan \/ Mengchen Fan<\/li>\n<li>\u65f6\u95f4\uff1a2026-05-25 arXiv v1\uff1b\u9875\u9762\u6807\u6ce8 accepted by MICCAI 2026<\/li>\n<li>\u6765\u6e90\uff1aMICCAI 2026 \/ arXiv preprint<\/li>\n<li>\u8bba\u6587\u9875\u9762\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2605.26382<\/li>\n<li>PDF \u6587\u4ef6 \/ PDF \u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2605.26382<\/li>\n<li>\u4ee3\u7801\u94fe\u63a5\uff1ahttps:\/\/github.com\/ClinicaAlpha\/DCD-3D-MedSeg<\/li>\n<li>\u4efb\u52a1\uff1a\u9ad8\u6548 3D MRI \u5206\u5272\uff1b\u538b\u7f29 nnU-Net\/VNet-like 3D U-Net student\uff0c\u540c\u65f6\u4fdd\u6301\u75c5\u7076\u8fb9\u754c\u548c\u5c0f\u7ed3\u6784<\/li>\n<li>\u6570\u636e\u96c6\uff1aBraTS 2024-BraTS-GLI\uff1bISLES 2022<\/li>\n<li>\u65b9\u6cd5\u7c7b\u578b\uff1a3D medical image segmentation\uff1bknowledge distillation\uff1bwavelet-domain detail distillation\uff1bnnU-Net compression<\/li>\n<\/ul>\n<h3>paper-deep-reader \u7cbe\u8bfb\u7ed3\u679c<\/h3>\n<h4>1. \u4e00\u53e5\u8bdd\u7ed3\u8bba<\/h4>\n<p>\u8fd9\u7bc7\u8bba\u6587\u6700\u6709\u4ef7\u503c\u7684\u5730\u65b9\u4e0d\u662f\u63d0\u51fa\u65b0 backbone\uff0c\u800c\u662f\u7ed9 3D nnU-Net \u538b\u7f29\u63d0\u4f9b\u4e86\u4e00\u4e2a\u6e05\u6670\u3001\u4f4e\u4fb5\u5165\u3001\u53ea\u5728\u8bad\u7ec3\u671f\u4f7f\u7528\u7684\u7ec6\u8282\u84b8\u998f\u6a21\u5757\uff1a\u53ea\u84b8\u998f wavelet directional detail subspace\uff0c\u4ee5\u63d0\u5347\u5c0f\u6a21\u578b\u5bf9\u8fb9\u754c\u548c\u5c0f\u75c5\u7076\u7684\u4fdd\u771f\u5ea6\uff0c\u540c\u65f6\u4e0d\u589e\u52a0\u63a8\u7406\u6210\u672c\u3002<\/p>\n<h4>2. \u7814\u7a76\u80cc\u666f\u4e0e\u6838\u5fc3\u95ee\u9898<\/h4>\n<p>\u8bba\u6587\u7814\u7a76\u538b\u7f29 3D MRI \u5206\u5272\u6a21\u578b\u65f6\u7684\u7ec6\u8282\u4e22\u5931\u95ee\u9898\u3002\u9ad8\u6027\u80fd 3D U-Net\/nnU-Net \u901a\u5e38\u53c2\u6570\u91cf\u548c FLOPs \u5f88\u5927\uff0c\u538b\u7f29\u6210\u5c0f\u6a21\u578b\u540e\u8fd8\u80fd\u4fdd\u7559\u5927\u4f53\u5b9a\u4f4d\uff0c\u4f46\u5bb9\u6613\u4e22\u5931\u8fb9\u754c\u3001\u5c0f\u75c5\u7076\u548c\u8584\u7ed3\u6784\u3002\u4f5c\u8005\u5c06\u5176\u5f52\u56e0\u4e8e\u4e24\u70b9\uff1aencoder \u591a\u7ea7 stride\/downsampling \u4f1a\u7d2f\u79ef\u635f\u5931\u9ad8\u9891\u4fe1\u606f\uff1b\u795e\u7ecf\u7f51\u7edc spectral bias \u4f7f\u4f4e\u9891\/\u5168\u5c40\u7ed3\u6784\u66f4\u5bb9\u6613\u62df\u5408\uff0c\u9ad8\u9891\u7ec6\u8282\u9700\u8981\u66f4\u591a\u5bb9\u91cf\u3002\u5bf9\u8111\u80bf\u7624\u5b50\u533a\u548c\u7f3a\u8840\u6027\u5352\u4e2d\u75c5\u7076\u800c\u8a00\uff0c\u8fb9\u754c\u548c\u5c0f\u7ed3\u6784\u8d28\u91cf\u76f4\u63a5\u5f71\u54cd\u5b9a\u91cf\u5206\u6790\uff0c\u540c\u65f6\u5b9e\u9645\u90e8\u7f72\u53c8\u53d7\u663e\u5b58\u4e0e\u5ef6\u8fdf\u9650\u5236\u3002<\/p>\n<p>Paper map\uff1a\u8bba\u6587\u7814\u7a76\u538b\u7f29 3D MRI \u5206\u5272\u6a21\u578b\u7684\u7ec6\u8282\u4fdd\u771f\u95ee\u9898\uff0c\u8bbe\u5b9a\u4e3a frozen high-capacity teacher \u4e0e channel-reduced nnU-Net student\u3002\u4e3b\u52a8\u4f5c\u662f\u7528 3D DWT\/IDWT \u5728\u6bcf\u4e2a encoder stage \u5bf9 teacher\/student feature \u505a directional detail subspace \u84b8\u998f\u3002\u4f5c\u8005\u58f0\u79f0\u8be5\u65b9\u6cd5\u6bd4\u5e38\u89c4 KD \u548c\u9891\u7387 KD \u66f4\u80fd\u6539\u5584 mDice\u3001HD95\u3001NSD\uff0c\u8bc1\u636e\u6765\u81ea BraTS 2024 \u4e0e ISLES 2022 \u7684\u5bf9\u6bd4\u3001\u6d88\u878d\u548c\u7edf\u8ba1\u68c0\u9a8c\u3002\u5173\u952e\u6280\u672f\u5bf9\u8c61\u662f encoder stage feature\u30013D-DWT subbands A\/D\/S\u3001detail projection PD\u3001stage-wise MSE loss\u3002\u771f\u6b63\u7684\u667a\u529b\u8d1f\u8f7d\u5728\u201c\u4e3a\u4ec0\u4e48\u53ea\u84b8\u998f D \u800c\u6392\u9664 A \u548c HHH\/S\u201d\u4ee5\u53ca\u201cstage-wise + IDWT \u662f\u5426\u771f\u6b63\u9694\u79bb\u4e86\u7ec6\u8282\u76d1\u7763\u201d\u3002\u4e3b\u8981\u5931\u8d25\u98ce\u9669\u662f\u5b9e\u9a8c\u53ea\u8986\u76d6\u4e24\u4e2a MRI \u6570\u636e\u96c6\u3001student \u538b\u7f29\u65b9\u5f0f\u5355\u4e00\uff0c\u4e14\u6536\u76ca\u53ef\u80fd\u4f9d\u8d56\u7279\u5b9a nnU-Net \u914d\u7f6e\u4e0e wavelet \u8d85\u53c2\u3002<\/p>\n<p>Route record\uff1aPrimary adapter = method-algorithm\uff1bSecondary adapter = \u65e0\uff1bEvidence packs = general + experimental-eval + ablation-and-mechanism-isolation + reproducibility-and-compute\uff1bDomain overlay = \u65e0\u3002\u8be5\u8def\u7ebf\u7684\u539f\u56e0\u662f\u8bba\u6587\u6838\u5fc3\u8d21\u732e\u662f\u8bad\u7ec3\u671f distillation mechanism\uff0c\u8bc1\u636e\u7531\u5bf9\u6bd4\u5b9e\u9a8c\u3001\u6d88\u878d\u4e0e\u590d\u6742\u5ea6\u8868\u652f\u6491\u3002<\/p>\n<h4>3. \u73b0\u6709\u65b9\u6cd5\u4e0d\u8db3<\/h4>\n<p>\u4f5c\u8005\u8ba4\u4e3a\u5e38\u89c4 KD \u5728 3D \u533b\u5b66\u5206\u5272\u4e2d\u6709\u4e09\u70b9\u4e0d\u8db3\u3002\u7b2c\u4e00\uff0c\u76f4\u63a5 match feature\/logits \u5bb9\u6613\u88ab\u9ad8\u80fd\u91cf\u4f4e\u9891\u7ed3\u6784\u4e3b\u5bfc\uff0c\u5c0f\u6a21\u578b\u53ef\u80fd\u5b66\u5230\u7c97\u5b9a\u4f4d\uff0c\u4f46\u8fb9\u754c\u548c\u7ec6\u5c0f\u7ed3\u6784\u4ecd\u7136\u8fc7\u5e73\u6ed1\u3002\u7b2c\u4e8c\uff0c\u81ea\u7136\u56fe\u50cf\u9891\u7387\u84b8\u998f\u4e0d\u80fd\u76f4\u63a5\u8fc1\u79fb\u5230 MRI\uff0c\u56e0\u4e3a MRI \u9ad8\u9891\u4e2d\u65e2\u6709\u8fb9\u754c\u4fe1\u606f\uff0c\u4e5f\u6709\u566a\u58f0\u548c\u626b\u63cf\u4f2a\u5f71\u3002\u7b2c\u4e09\uff0c\u538b\u7f29 3D encoder \u7684\u591a\u7ea7\u4e0b\u91c7\u6837\u5c24\u5176\u635f\u5bb3\u5c0f\u7ed3\u6784\uff0c\u666e\u901a feature distillation \u5f88\u96be\u4e13\u95e8\u4fee\u590d\u8fd9\u4e2a\u95ee\u9898\u3002\u5b83\u4e0e U-Net\/nnU-Net \u7684\u5173\u7cfb\u5f88\u76f4\u63a5\uff1aDCD \u4e0d\u66ff\u4ee3 nnU-Net\uff0c\u800c\u662f\u5728 teacher-student nnU-Net \u538b\u7f29\u573a\u666f\u4e2d\u52a0\u5165\u8bad\u7ec3\u671f feature-level regularization\u3002<\/p>\n<h4>4. \u65b9\u6cd5\u603b\u89c8<\/h4>\n<p>DCD \u7684\u6d41\u7a0b\u662f\uff1a1\uff09\u7528\u5b8c\u6574\u5bb9\u91cf nnU-Net-like teacher\uff0c\u51bb\u7ed3\u53c2\u6570\uff1bstudent \u91c7\u7528\u76f8\u540c encoder-decoder topology\uff0c\u4f46\u6309 reduction factor r=4 \u505a uniform channel reduction\u30022\uff09\u5bf9\u6bcf\u4e2a encoder stage i\uff0c\u53d6 teacher feature Fi(t) \u4e0e student feature Fi(s)\u30023\uff09\u7528 learnable 1x1x1 projection phi_i \u5bf9\u9f50 channel\u30024\uff09\u5bf9 feature \u6309 channel \u72ec\u7acb\u505a 3D DWT\uff0c\u5f97\u5230 LLL\u3001LLH\u3001LHL\u3001LHH\u3001HLL\u3001HLH\u3001HHL\u3001HHH \u516b\u7c7b\u5b50\u5e26\u30025\uff09\u5212\u5206\u4e3a\u4f4e\u9891\u8fd1\u4f3c A={LLL}\u3001\u6781\u7aef\u9ad8\u9891\/\u566a\u58f0\u654f\u611f S={HHH}\u3001\u65b9\u5411\u6027\u7ec6\u8282 D={L,H}^3 \\ {LLL, HHH}\u30026\uff09\u53ea\u4fdd\u7559 D \u5b50\u5e26\uff0c\u5176\u4f59\u7f6e\u96f6\uff0c\u5e76\u7528 IDWT \u91cd\u5efa spatial-domain detail-only feature\u30027\uff09\u5bf9 teacher \u4e0e projected student \u7684 detail-only reconstruction \u505a MSE\uff0c\u5e76\u5728\u6240\u6709 encoder stages \u6c42\u548c\u30028\uff09\u603b\u635f\u5931\u4e3a Ltotal = Lseg + mu LDCD\uff0c\u5176\u4e2d Lseg \u662f Dice + cross-entropy\uff0c\u8bba\u6587\u8bbe\u7f6e mu=0.05\uff1bDWT\/IDWT \u548c projection \u53ea\u5728\u8bad\u7ec3\u671f\u4f7f\u7528\u3002<\/p>\n<p>\u6838\u5fc3\u516c\u5f0f\u53ef\u6982\u62ec\u4e3a\uff1aDWT \u5c06 F \u5206\u89e3\u4e3a beta in {L,H}^3 \u7684\u5b50\u5e26\uff1bA={LLL}\uff0cS={HHH}\uff0cD={L,H}^3{LLL,HHH}\u3002detail projection PD(F) \u662f\u53ea\u4fdd\u7559 D \u5b50\u5e26\u540e\u7ecf IDWT \u91cd\u5efa\u7684\u7279\u5f81\u3002\u6bcf\u5c42\u635f\u5931 LDCD(i)=||PD(Fi(t))-PD(phi_i(Fi(s)))||^2\/Ni\uff0c\u603b LDCD \u4e3a\u591a stage \u6c42\u548c\u3002<\/p>\n<h4>5. \u6838\u5fc3\u6a21\u5757\u62c6\u89e3<\/h4>\n<p><strong>Wavelet Detail Subspace Selection<\/strong>\uff1a\u8f93\u5165\u67d0\u4e00 encoder stage \u7684 3D feature tensor\uff0c\u8f93\u51fa\u65b9\u5411\u6027\u7ec6\u8282\u5b50\u5e26 D\u3002\u5b83\u907f\u514d\u4f4e\u9891 A \u652f\u914d\u84b8\u998f\uff0c\u540c\u65f6\u907f\u514d HHH \u8fd9\u79cd\u4e09\u8f74\u5168\u9ad8\u9891\u5b50\u5e26\u628a MRI \u566a\u58f0\/\u4f2a\u5f71\u6ce8\u5165 student\u3002\u521b\u65b0\u6027\u4e0d\u662f\u65b0\u6570\u5b66\u5de5\u5177\uff0c\u800c\u662f\u533b\u5b66\u6210\u50cf\u52a8\u673a\u660e\u786e\u7684\u9009\u62e9\u6027\u9891\u7387\u84b8\u998f\u3002<\/p>\n<p><strong>IDWT spatial-domain reconstruction<\/strong>\uff1a\u8f93\u5165\u53ea\u4fdd\u7559 D \u5b50\u5e26\u540e\u7684 wavelet coefficients\uff0c\u8f93\u51fa\u4e0e\u539f feature \u540c\u7a7a\u95f4\u51e0\u4f55\u5bf9\u9f50\u7684 detail-only feature\u3002\u5b83\u907f\u514d\u76f4\u63a5\u5728 coefficient layout \u4e0a\u505a loss \u7684\u5b9e\u73b0\u4f9d\u8d56\u3002\u6d88\u878d\u663e\u793a\u53bb\u6389 IDWT \u540e BraTS mDice \u4ece 68.51 \u964d\u5230 62.87\uff0cISLES \u4ece 73.95 \u964d\u5230 70.06\uff0c\u8bf4\u660e\u8be5\u6a21\u5757\u4e0d\u662f\u88c5\u9970\u3002<\/p>\n<p><strong>Stage-wise distillation<\/strong>\uff1a\u5728\u6bcf\u4e2a encoder stage \u76d1\u7763 detail\uff0c\u56e0\u4e3a\u7ec6\u8282\u4e22\u5931\u968f\u591a\u7ea7 downsampling \u7d2f\u79ef\u3002\u5b83\u53ef\u8fc1\u79fb\u5230\u6709\u591a\u7ea7 encoder feature \u7684 U-Net\u3001UNetR\u3001Swin-UNet\u3001MedNeXt\u3001SegMamba\/DAMamba \u7b49\u6846\u67b6\u3002<\/p>\n<p>\u5bf9 polyp segmentation\uff1a\u53ef\u6539\u6210 2D wavelet detail distillation\uff0c\u4f46\u5185\u955c\u56fe\u50cf\u9ad8\u9891\u542b\u53cd\u5149\u3001\u7eb9\u7406\u3001\u8fd0\u52a8\u6a21\u7cca\uff0c\u4e0d\u80fd\u76f4\u63a5\u7167\u642c MRI \u4e2d\u6392\u9664 HHH \u7684\u5047\u8bbe\u3002\u5bf9 3D medical image segmentation\uff1a\u5f88\u9002\u5408 brain tumor\u3001stroke\u3001vessel\u3001small organ\u3001lesion segmentation \u7b49\u8fb9\u754c\u654f\u611f\u4efb\u52a1\u3002<\/p>\n<h4>6. \u5b9e\u9a8c\u8bbe\u8ba1\u4e0e\u7ed3\u679c<\/h4>\n<p>\u6570\u636e\u96c6\u5305\u62ec BraTS 2024-BraTS-GLI\uff081350 cases\uff0c1080 train \/ 270 validation\uff0c\u56db\u6a21\u6001 MRI\uff0cET\/NETC\/SNFH\/RC \u5b50\u533a\uff09\u548c ISLES 2022\uff08250 cases\uff0c200 train \/ 50 validation\uff0cADC\/DWI\/FLAIR\uff0c\u5355\u7c7b\u5352\u4e2d\u75c5\u7076\uff09\u3002\u5b9e\u73b0\u4e0a teacher\/student \u90fd\u9075\u5faa nnU-Net encoder\/decoder topology\uff1bstudent \u7528 r=4 channel reduction\uff1bDWT \u4f7f\u7528 Daubechies-4\uff0clevel=3\uff1b\u5355\u5361 A100\uff0cSGD + Nesterov\uff0clr=0.01\uff0cweight decay=3e-5\u3002<\/p>\n<p>\u4e3b\u8981\u7ed3\u679c\uff1aBraTS 2024 overall \u4e2d w\/o KD mDice 63.60\uff0cDCD 68.51\uff0c\u63d0\u5347 +4.91\uff1b\u76f8\u6bd4 IFVD 66.54\uff0c\u63d0\u5347 +1.97\uff0c\u5e76\u62a5\u544a paired t-test p=0.0078\u3001Wilcoxon p=0.0080\u3002ISLES 2022 \u4e2d w\/o KD mDice 70.21\uff0cDCD 73.95\uff0c\u63d0\u5347 +3.74\u3002BraTS \u5b50\u533a\u4e2d NETC \u4ece 41.26 \u63d0\u5347\u5230 54.36\uff0c\u8bf4\u660e\u56f0\u96be\u5c0f\u7ed3\u6784\u53d7\u76ca\u660e\u663e\u3002\u590d\u6742\u5ea6\u65b9\u9762\uff0cteacher \u7ea6 102M \u53c2\u6570\u300117\u201319 TFLOPs\uff1bstudent \u7ea6 6.4M \u53c2\u6570\u30011.1\u20131.3 TFLOPs\uff1bDCD \u4e0d\u589e\u52a0\u63a8\u7406\u5f00\u9500\u3002\u6d88\u878d\u663e\u793a\u53ea\u84b8\u998f D \u6700\u4f18\uff0c\u84b8\u998f S \u5728 BraTS \u4e0a\u751a\u81f3\u4f4e\u4e8e w\/o KD\uff0c\u53bb\u6389 IDWT \u660e\u663e\u4e0b\u964d\u3002<\/p>\n<h4>7. \u5b9e\u9a8c\u53ef\u4fe1\u5ea6\u5224\u65ad<\/h4>\n<p>\u53ef\u4fe1\u70b9\uff1a\u5bf9\u6bd4\u5bf9\u8c61\u5305\u62ec w\/o KD\u3001logits KD\u3001feature KD\u3001CWD\u3001IFVD\u3001FreeKD\uff1b\u6307\u6807\u5305\u62ec mDice\u3001HD95\u3001NSD\uff0c\u5e76\u62a5\u544a mean \u00b1 standard error\uff1b\u5173\u952e\u63d0\u5347\u6709\u7edf\u8ba1\u68c0\u9a8c\uff1b\u6d88\u878d\u76f4\u63a5\u9a8c\u8bc1 A\/D\/S band selection \u548c IDWT\uff1b\u590d\u6742\u5ea6\u8868\u8bc1\u660e\u90e8\u7f72\u7aef\u786e\u5b9e\u662f\u5c0f\u6a21\u578b\u63a8\u7406\u3002<\/p>\n<p>\u4e3b\u8981 caveat\uff1a\u6570\u636e\u96c6\u53ea\u8986\u76d6 brain\/stroke MRI\uff0c\u4e0d\u7b49\u4e8e\u6240\u6709 3D \u533b\u5b66\u5206\u5272\uff1bstudent \u53ea\u7528 uniform channel reduction r=4\uff0c\u4e0d\u6e05\u695a\u5bf9 MedNeXt-small\u3001Transformer\/Mamba student \u662f\u5426\u540c\u6837\u6709\u6548\uff1bwavelet basis\/level \u7f3a\u5c11\u7cfb\u7edf\u654f\u611f\u6027\u5206\u6790\uff1bISLES \u4e0a DCD \u7684 HD95 \u4e0d\u4f18\u4e8e CWD\uff0c\u56e0\u6b64\u201c\u8fb9\u754c\u6307\u6807\u5168\u9762\u66f4\u5f3a\u201d\u7684\u8bf4\u6cd5\u4e0d\u80fd\u8fc7\u5ea6\u6cdb\u5316\u3002\u603b\u4f53\u4e0a\uff0c\u8bc1\u636e\u8db3\u4ee5\u652f\u6301\u201cDCD \u662f\u6709\u7528\u7684 3D MRI student training loss\u201d\uff0c\u4f46\u4e0d\u8db3\u4ee5\u652f\u6301\u201c\u5df2\u9a8c\u8bc1\u901a\u7528\u533b\u5b66\u56fe\u50cf\u5206\u5272\u538b\u7f29\u65b9\u6848\u201d\u3002<\/p>\n<h4>8. \u4e0e\u4e3b\u6d41\u533b\u5b66\u56fe\u50cf\u5206\u5272\u6846\u67b6\u7684\u5173\u7cfb<\/h4>\n<p>DCD \u662f nnU-Net\/3D U-Net \u538b\u7f29\u8bad\u7ec3\u7b56\u7565\uff0c\u4e0d\u662f\u66ff\u4ee3\u6846\u67b6\u3002\u5b83\u53ef\u4f5c\u4e3a MedNeXt\u3001UNetR\/Swin-UNet\u3001TransUNet\/TransFuse\u3001SegMamba\/DAMamba \u7684\u8bad\u7ec3\u671f\u8f85\u52a9 loss\uff0c\u4f46 Transformer\/Mamba token feature \u7684\u9891\u7387\u89e3\u91ca\u9700\u8981\u91cd\u505a\u3002\u4e0e MedSAM\/foundation model \u7684\u5173\u7cfb\u662f\uff1a\u53ef\u7528\u4e8e\u628a\u5f3a teacher \u6216\u5927\u6a21\u578b\u84b8\u998f\u5230\u8f7b\u91cf 3D student\u3002<\/p>\n<h4>9. \u5bf9\u6211\u8bfe\u9898\u7684\u4ef7\u503c<\/h4>\n<p>\u4ef7\u503c\u8f83\u9ad8\u3002\u5b83\u9002\u5408\u4f5c\u4e3a 3D segmentation \u538b\u7f29\/\u90e8\u7f72 baseline \u6216\u8bad\u7ec3\u6280\u5de7\uff1b\u9002\u5408 DAMamba \u6539\u9020\uff0c\u5373\u7528\u5927 DAMamba\/SegMamba teacher \u84b8\u998f\u8f7b\u91cf Mamba-U-Net student\uff1b\u4e5f\u9002\u5408\u542f\u53d1 2D polyp segmentation \u7684 wavelet detail distillation + boundary-aware loss\uff0c\u4f46\u9700\u8981\u91cd\u65b0\u9a8c\u8bc1\u5185\u955c\u9891\u8c31\u5047\u8bbe\u3002related work \u53ef\u653e\u5728 efficient 3D medical segmentation\u3001knowledge distillation\u3001frequency-domain supervision \u4e09\u6761\u7ebf\u3002<\/p>\n<h4>10. \u9605\u8bfb\u5efa\u8bae<\/h4>\n<p><strong>\u5f3a\u70c8\u5efa\u8bae\u7cbe\u8bfb\u3002<\/strong> \u673a\u5236\u5e72\u51c0\u3001\u5b9e\u73b0\u6210\u672c\u4f4e\u3001\u6d88\u878d\u76f4\u6307\u6838\u5fc3\u5047\u8bbe\u3002\u5efa\u8bae\u4f18\u5148\u8bfb Method 2.2\u20132.3\u3001Table 1\u20134\uff0c\u5e76\u68c0\u67e5\u4ee3\u7801\u4e2d DWT\/IDWT loss \u5b9e\u73b0\u3002<\/p>\n<hr \/>\n<h2>\u8bba\u6587 2\uff1aAre We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?<\/h2>\n<h3>\u57fa\u672c\u4fe1\u606f<\/h3>\n<ul>\n<li>\u6807\u9898\uff1aAre We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?<\/li>\n<li>\u4f5c\u8005 \/ \u7b2c\u4e00\u4f5c\u8005\uff1aJun Li, Ziwei Qin \/ Jun Li<\/li>\n<li>\u65f6\u95f4\uff1a2026-05-25 arXiv v1\uff1b\u8bba\u6587\u9996\u9875\u6807\u6ce8 Proceedings of ICML 2026, PMLR 306<\/li>\n<li>\u6765\u6e90\uff1aICML 2026 \/ arXiv preprint<\/li>\n<li>\u8bba\u6587\u9875\u9762\u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/abs\/2605.25561<\/li>\n<li>PDF \u6587\u4ef6 \/ PDF \u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2605.25561<\/li>\n<li>\u4ee3\u7801\u94fe\u63a5\uff1ahttps:\/\/github.com\/DirkLiii\/TCSeg<\/li>\n<li>\u4efb\u52a1\uff1a\u534a\u76d1\u7763 3D \u533b\u5b66\u56fe\u50cf\u5206\u5272\uff1b\u4f2a\u6807\u7b7e\u53ef\u9760\u6027\u6821\u51c6\uff1b\u591a\u6b21\u8fd0\u884c\u4e0e checkpoint protocol \u8bc4\u4f30<\/li>\n<li>\u6570\u636e\u96c6\uff1aLeft Atrium (LA)\u3001Pancreas-CT NIH\u3001BraTS2019<\/li>\n<li>\u65b9\u6cd5\u7c7b\u578b\uff1asemi-supervised 3D medical image segmentation\uff1bdual-axis reliability estimation\uff1btri-space calibration\uff1bevaluation protocol critique<\/li>\n<\/ul>\n<h3>paper-deep-reader \u7cbe\u8bfb\u7ed3\u679c<\/h3>\n<h4>1. \u4e00\u53e5\u8bdd\u7ed3\u8bba<\/h4>\n<p>\u8fd9\u7bc7\u8bba\u6587\u7684\u6700\u5927\u4ef7\u503c\u5728\u4e8e\u540c\u65f6\u6307\u51fa\u534a\u76d1\u7763\u533b\u5b66\u5206\u5272\u7684\u4e24\u4e2a\u201c\u8fc7\u5ea6\u81ea\u4fe1\u201d\uff1a\u6a21\u578b\u628a\u9ad8 softmax confidence \u5f53\u6210\u53ef\u9760\u4f2a\u6807\u7b7e\uff0c\u4ee5\u53ca\u793e\u533a\u628a single-run best checkpoint \u5f53\u6210\u771f\u5b9e\u6027\u80fd\uff1bTCSeg \u662f\u5bf9\u5e94\u7684\u53ef\u9760\u6027\u5efa\u6a21\u65b9\u6cd5\uff0cmulti-run best\/last protocol \u662f\u66f4\u91cd\u8981\u7684\u5b9e\u9a8c\u89c4\u8303\u63d0\u9192\u3002<\/p>\n<h4>2. \u7814\u7a76\u80cc\u666f\u4e0e\u6838\u5fc3\u95ee\u9898<\/h4>\n<p>\u533b\u5b66\u56fe\u50cf\u5206\u5272\u6807\u6ce8\u6602\u8d35\uff0c\u56e0\u6b64\u534a\u76d1\u7763\u5b66\u4e60\u5e38\u7528 pseudo-labeling\u3001consistency regularization\u3001teacher-student \u6216 multi-branch co-training\u3002\u4f5c\u8005\u6307\u51fa\u4e3b\u6d41 SSL segmentation \u8fc7\u5ea6\u4f9d\u8d56\u201c\u9ad8\u7f6e\u4fe1\u5ea6=\u6b63\u786e\u201d\u7684\u5047\u8bbe\uff0c\u4f46\u6df1\u5ea6\u7f51\u7edc\u53ef\u80fd confidently wrong\uff0c\u5c24\u5176\u5728\u5668\u5b98\u8fb9\u754c\u3001\u4f4e\u5bf9\u6bd4\u7ed3\u6784\u3001\u5c11\u6807\u6ce8\u573a\u666f\u4e2d\uff0c\u9519\u8bef\u4f2a\u6807\u7b7e\u4f1a\u88ab\u53cd\u590d\u5f3a\u5316\uff0c\u5f62\u6210 confirmation bias\u3002\u8bba\u6587\u8fd8\u6279\u8bc4\u8bc4\u4f30\u5c42\u9762\u7684\u8fc7\u5ea6\u4e50\u89c2\uff1a\u8bb8\u591a\u6570\u636e\u96c6\u6ca1\u6709\u72ec\u7acb validation set\uff0c\u7814\u7a76\u8005\u53ef\u80fd\u7528 test set \u505a checkpoint selection\uff0c\u518d\u62a5\u544a single-run best checkpoint\uff0c\u9020\u6210 SOTA \u6570\u5b57\u88ab\u9ad8\u4f30\u3002<\/p>\n<p>Paper map\uff1a\u8bba\u6587\u7814\u7a76\u534a\u76d1\u7763 3D \u533b\u5b66\u5206\u5272\u4e2d\u7684\u4f2a\u6807\u7b7e\u53ef\u9760\u6027\u4e0e\u7ed3\u679c\u62a5\u544a\u8fc7\u5ea6\u81ea\u4fe1\u95ee\u9898\uff0c\u8bbe\u5b9a\u4e3a VNet-style shared encoder + dual decoder + EMA teacher \u7684 SSL \u6846\u67b6\u3002\u4e3b\u52a8\u4f5c\u662f\u628a reliability \u62c6\u6210 confidence \u4e0e uncertainty \u4e24\u8f74\uff0c\u5e76\u5728 probability\/feature\/image \u4e09\u4e2a\u7a7a\u95f4\u6821\u51c6\u4f2a\u6807\u7b7e\u548c\u6270\u52a8\u8bad\u7ec3\u3002\u4f5c\u8005\u58f0\u79f0 TCSeg \u80fd\u7a33\u5b9a\u63d0\u5347 LA\u3001Pancreas-CT\u3001BraTS2019 \u8868\u73b0\uff0c\u5e76\u4e14 multi-run best\/last \u66f4\u80fd\u63ed\u793a\u771f\u5b9e\u7a33\u5b9a\u6027\u3002\u5173\u952e\u6280\u672f\u5bf9\u8c61\u662f C(v)\u3001Upro(v)\u3001Ufea(v)\u3001prototype similarity q(v)\u3001C+U- mask\u3001Lpse\u3001Lcal\u3001Lmix\u3002\u667a\u529b\u8d1f\u8f7d\u5728 reliability decoupling \u662f\u5426\u771f\u6b63\u51cf\u5c11 confidently wrong pseudo-label\uff0c\u4ee5\u53ca evaluation protocol \u662f\u5426\u6539\u53d8 SOTA \u89e3\u91ca\u3002\u4e3b\u8981\u5931\u8d25\u98ce\u9669\u662f\u65b9\u6cd5\u6a21\u5757\u8f83\u591a\uff0c\u6536\u76ca\u53ef\u80fd\u6765\u81ea\u989d\u5916\u6b63\u5219\/\u589e\u5f3a\u7ec4\u5408\uff0c\u800c\u975e\u5355\u72ec\u6765\u81ea confidence-uncertainty \u89e3\u8026\uff1b\u540c\u65f6\u5386\u53f2\u65b9\u6cd5\u5c1a\u672a\u7edf\u4e00\u91cd\u8dd1\u3002<\/p>\n<p>Route record\uff1aPrimary adapter = method-algorithm\uff1bSecondary adapter = benchmark-evaluation\uff08\u8bc4\u4f30\u534f\u8bae\u6279\u5224\u662f load-bearing \u8d21\u732e\uff09\uff1bEvidence packs = general + experimental-eval + ablation-and-mechanism-isolation + reproducibility-and-compute\uff1bDomain overlay = \u65e0\u3002<\/p>\n<h4>3. \u73b0\u6709\u65b9\u6cd5\u4e0d\u8db3<\/h4>\n<p>\u4f5c\u8005\u6279\u8bc4\u73b0\u6709\u534a\u76d1\u7763 3D \u533b\u5b66\u5206\u5272\u65b9\u6cd5\uff1a1\uff09\u628a confidence \u548c uncertainty \u6df7\u6210\u4e00\u4e2a\u6807\u91cf\uff0csoftmax max probability\u3001entropy \u6216 variance \u4e0d\u8db3\u4ee5\u4ee3\u8868\u4f2a\u6807\u7b7e\u53ef\u9760\u6027\uff1b2\uff09\u9519\u8bef\u9884\u6d4b\u4e00\u65e6\u88ab confidence threshold \u9009\u4e2d\uff0c\u4f1a\u901a\u8fc7 cross-entropy \u88ab\u63a8\u5411 simplex vertex\uff0c\u5f62\u6210 confirmation bias\uff1b3\uff09EMA teacher \u4e0e student \u9519\u8bef\u76f8\u5173\u6027\u5f3a\uff0c\u591a\u6837\u6027\u6709\u9650\uff1b4\uff09single-run best checkpoint \u5c24\u5176\u5728\u65e0\u9a8c\u8bc1\u96c6\u6570\u636e\u96c6\u4e0a\u53ef\u80fd\u9690\u542b test overfitting\u3002\u5b83\u63d0\u9192\u6240\u6709 U-Net\u3001nnU-Net\u3001Transformer\u3001Mamba\u3001foundation model \u7684\u534a\u76d1\u7763\u5b9e\u9a8c\uff1a\u53ea\u62a5\u5355 seed best checkpoint \u53ef\u4fe1\u5ea6\u4e0d\u8db3\u3002<\/p>\n<h4>4. \u65b9\u6cd5\u603b\u89c8<\/h4>\n<p>TCSeg \u5305\u542b dual-axis reliability estimation \u548c tri-space calibration\u3002\u5bf9\u6bcf\u4e2a voxel v\uff0c\u5b9a\u4e49 reliability vector R(v)=<C(v),U(v)>\uff0c\u5176\u4e2d U(v)=<Upro(v),Ufea(v)>\u3002C(v) \u662f student \u4e24\u4e2a decoder \u4e0e EMA teacher \u4e24\u4e2a decoder \u7684 ensemble mean prediction \u6700\u5927\u503c\uff1bUpro(v) \u662f\u4e24\u4e2a decoder probability output \u7684 L1 disagreement\uff1bUfea(v) \u662f\u4e24\u4e2a decoder \u5728 prototype similarity prediction q(v) \u4e0a\u7684 L1 disagreement\u3002q(v) \u6765\u81ea voxel embedding \u4e0e class prototype \u7684 cosine similarity\uff0cprototype \u7531\u9ad8\u7f6e\u4fe1 voxel embedding \u5e73\u5747\u5f97\u5230\u3002<\/p>\n<p>Tri-space calibration \u5305\u62ec\uff1aprobability space \u4e2d\u53ea\u5bf9\u9ad8 confidence \u4e14\u4f4e uncertainty \u7684 voxel \u65bd\u52a0\u4f2a\u6807\u7b7e\u76d1\u7763\uff0c\u5e76\u7528\u4e0a\u4e0b confidence bounds \u6784\u9020 positive\/negative supervision\uff1bfeature space \u4e2d\u8ba9 probability output \u4e0e prototype similarity prediction \u4e00\u81f4\uff0c\u4f7f\u9ad8\u7f6e\u4fe1\u5fc5\u987b\u6709\u8bed\u4e49\u5d4c\u5165\u652f\u6301\uff1bimage space \u4e2d\u6839\u636e reliability mask \u627e\u5230 cognitive blind spots\uff0c\u5bf9\u4e0d\u53ef\u9760\u533a\u57df\u505a targeted CutMix\u3002\u6574\u4f53\u635f\u5931\u4e3a Ltotal = Lsup + Lpse + Lcal + Lmix\u3002\u7f51\u7edc\u4e3a shared five-stage encoder + two parallel decoders\uff0cEMA encoder\/decoders \u63d0\u4f9b teacher views\u3002<\/p>\n<h4>5. \u6838\u5fc3\u6a21\u5757\u62c6\u89e3<\/h4>\n<p><strong>Confidence score C(v)<\/strong>\uff1a\u8f93\u5165 student\/teacher \u591a\u4e2a decoder \u6982\u7387\u56fe\uff0c\u8f93\u51fa ensemble mean prediction \u7684\u6700\u5927\u7c7b\u522b\u6982\u7387\u3002\u5b83\u4e0d\u518d\u5355\u72ec\u51b3\u5b9a\u53ef\u9760\u6027\uff0c\u53ea\u662f reliability \u7684\u4e00\u8f74\u3002<\/p>\n<p><strong>Uncertainty score U(v)<\/strong>\uff1a\u8f93\u5165\u53cc decoder \u7684 probability outputs \u4e0e prototype similarity outputs\uff0c\u8f93\u51fa probability disagreement \u4e0e feature disagreement\u3002\u5b83\u68c0\u6d4b\u201c\u9ad8 softmax \u4f46\u5206\u652f\/\u7279\u5f81\u8bc1\u636e\u4e0d\u7a33\u5b9a\u201d\u7684 voxel\u3002<\/p>\n<p><strong>Probability-space pseudo supervision<\/strong>\uff1a\u53ea\u8ba9 high-confidence\/low-uncertainty \u7684 C+U- \u533a\u57df\u53c2\u4e0e\u4f2a\u76d1\u7763\uff0c\u907f\u514d confident but uncertain voxel \u8fdb\u5165 pseudo-label training\u3002<\/p>\n<p><strong>Feature-space calibration Lcal<\/strong>\uff1a\u8ba9 probability prediction \u548c prototype-based semantic affinity \u4e00\u81f4\u3002\u6d88\u878d\u663e\u793a\u53bb\u6389 feature space \u5728 Pancreas-CT \u4e0a\u4f24\u5bb3\u5c24\u5176\u5927\uff0c\u8bf4\u660e\u5bf9\u4f4e\u5bf9\u6bd4\u3001\u8fb9\u754c\u6a21\u7cca\u5668\u5b98\u91cd\u8981\u3002<\/p>\n<p><strong>Image-space reliability-driven CutMix Lmix<\/strong>\uff1a\u7531\u4f4e confidence\/\u9ad8 uncertainty \u7b49\u4e0d\u53ef\u9760\u533a\u57df\u751f\u6210 perturbation mask\uff0c\u5bf9\u96be\u533a\u57df\u505a targeted CutMix\uff0c\u8ba9\u6a21\u578b\u91cd\u5b66\u7ed3\u6784\u7279\u5f81\u3002<\/p>\n<p>\u5355\u4e2a\u7ec4\u4ef6\u5e76\u975e\u5168\u65b0\uff0c\u521b\u65b0\u66f4\u5728\u4e8e\u628a confidence\/uncertainty \u663e\u5f0f\u62c6\u6210\u53cc\u8f74\uff0c\u5e76\u7528\u540c\u4e00\u4e2a reliability engine \u8fde\u63a5 probability\u3001feature\u3001image \u4e09\u7a7a\u95f4\u3002\u5bf9 semi-supervised polyp segmentation \u548c 3D \u5c11\u6807\u6ce8\u4efb\u52a1\u90fd\u6709\u8fc1\u79fb\u4ef7\u503c\uff0c\u4f46\u9608\u503c\u4e0e prototype \u8bbe\u8ba1\u9700\u8981\u6309\u56fe\u50cf\u57df\u91cd\u8c03\u3002<\/p>\n<h4>6. \u5b9e\u9a8c\u8bbe\u8ba1\u4e0e\u7ed3\u679c<\/h4>\n<p>\u6570\u636e\u96c6\u5305\u62ec LA\uff08100 \u4e2a gadolinium-enhanced cardiac MRI volumes\uff0c8\/72\u300116\/64 labeled\/unlabeled split\uff09\u3001Pancreas-CT NIH\uff0882 \u4e2a contrast-enhanced abdominal CT\uff0c6\/56\u300112\/50 split\uff09\u548c BraTS2019\uff08335 glioma subjects\uff0cwhole-tumor segmentation with FLAIR\uff0c250\/25\/60 train\/validation\/test\uff09\u3002backbone \u662f VNet-style shared five-stage encoder + two parallel decoders\u3002\u8bad\u7ec3\u4f7f\u7528 PyTorch\u3001NVIDIA RTX 4080\u3001SGD 20k iterations\u3001lr=0.01\u3001batch size 4\uff0c\u5e76\u91c7\u7528 sliding-window crop\/inference\u3002\u6307\u6807\u4e3a DSC\u3001ASD\u300195HD\u3002<\/p>\n<p>\u8bba\u6587\u91cd\u70b9\u662f\u8bc4\u4f30\u534f\u8bae\uff1a\u6bcf\u4e2a\u8bbe\u7f6e\u8dd1 5 \u4e2a random seeds\uff0c\u540c\u65f6\u62a5\u544a best checkpoint \u548c last checkpoint\uff1bmedian \u8868\u793a\u5178\u578b\u8868\u73b0\uff0cmaximum \u4fdd\u7559\u4e0e\u65e7 single-run best \u98ce\u683c\u53ef\u6bd4\u7684\u4e0a\u754c\u3002<\/p>\n<p>\u4e3b\u8981\u7ed3\u679c\uff1alast protocol \u4e0b\uff0cPancreas-CT 10% labeled TCSeg median DSC 81.08\uff0c\u9ad8\u4e8e TraCoCo 79.22\uff1bPancreas-CT 20% labeled median 83.44\uff0c\u9ad8\u4e8e TraCoCo 81.80\u3002LA 10% last median 90.28\uff0c\u9ad8\u4e8e TraCoCo 89.29 \u548c ARCO-SG 89.90\uff1bLA 20% last median 90.83\uff0c\u7565\u4f4e\u4e8e AUA 91.08 \u548c SFR 91.00\uff0c\u4f46 maximum 91.36\u3002BraTS2019 10% median 85.27\uff0c\u4f4e\u4e8e TraCoCo 85.71\uff0c\u4f46 maximum 86.52\uff1b20% median 86.47\uff0c\u7565\u4f4e\u4e8e TraCoCo 86.69\u3002<\/p>\n<p>\u6d88\u878d\uff1aw\/o U mean DSC 85.68\uff0cw\/o C mean 85.20\uff0cDual-axis 86.23\u3002\u4e09\u7a7a\u95f4\u6d88\u878d\u4e2d Only supervised mean 72.69\uff1bw\/o probability 85.13\uff1bw\/o image 84.00\uff1bw\/o feature 80.09\uff1bOurs 86.23\u3002\u8ba1\u7b97\u6210\u672c\u65b9\u9762\uff0cBraTS2019 \u4e0a TCSeg 12.34M \u53c2\u6570\uff0c0.421 s\/iter\uff0c\u6d4b\u8bd5 1.66 s\/case\uff0c10.83GB memory\uff1b\u8bad\u7ec3\u6210\u672c\u4f4e\u4e8e CC-Net \u7684 2.934 s\/iter\uff0c\u4f46\u663e\u5b58\u9ad8\u4e8e DTC\/CauSSL\u3002<\/p>\n<h4>7. \u5b9e\u9a8c\u53ef\u4fe1\u5ea6\u5224\u65ad<\/h4>\n<p>\u53ef\u4fe1\u70b9\uff1a\u8bba\u6587\u660e\u786e\u8ba8\u8bba best vs last\u3001median vs maximum\uff0c\u6bd4\u8bb8\u591a\u534a\u76d1\u7763\u5206\u5272\u8bba\u6587\u900f\u660e\uff1b\u6d88\u878d\u8986\u76d6 dual-axis\u3001\u4e09\u7a7a\u95f4\u3001\u53c2\u6570\u654f\u611f\u6027\u548c\u8ba1\u7b97\u6210\u672c\uff1b\u6570\u636e\u96c6\u8986\u76d6 MRI \u5fc3\u810f\u3001CT \u80f0\u817a\u3001\u8111\u80bf\u7624 MRI\uff1b\u4f5c\u8005\u660e\u786e\u627f\u8ba4\u5c40\u9650\uff0c\u5305\u62ec\u4e0d\u4ee3\u8868 OOD robustness \u6216 clinical readiness\u3001\u56fa\u5b9a\u9608\u503c\u4ecd\u9700\u6539\u8fdb\u3001\u5386\u53f2\u65b9\u6cd5\u5c1a\u672a\u7edf\u4e00\u91cd\u8dd1\u3002<\/p>\n<p>\u4e3b\u8981 caveat\uff1a\u867d\u7136\u8bba\u6587\u6279\u8bc4\u65e7\u65b9\u6cd5\u534f\u8bae\u4e0d\u7edf\u4e00\uff0c\u4f46 Table 1 \u4e2d\u5927\u91cf baseline \u4ecd\u6765\u81ea\u4e0d\u540c protocol\/\u6587\u732e\u62a5\u544a\uff0c\u5e76\u975e\u5168\u90e8\u7edf\u4e00\u91cd\u8dd1\uff1bTCSeg \u6a21\u5757\u8f83\u591a\uff0c\u6027\u80fd\u63d0\u5347\u53ef\u80fd\u6765\u81ea multi-decoder\u3001prototype loss\u3001CutMix\u3001threshold tuning \u7684\u7ec4\u5408\uff1b\u90e8\u5206\u6570\u636e\u96c6 median \u5e76\u4e0d\u603b\u662f\u8d85\u8d8a\u6700\u5f3a baseline\uff0c\u56e0\u6b64\u5e94\u8868\u8ff0\u4e3a\u201c\u66f4\u7a33\u5b9a\u4e14\u5728\u82e5\u5e72\u8bbe\u7f6e\u6709\u4f18\u52bf\u201d\uff0c\u800c\u975e\u5168\u9762 SOTA\uff1b\u8de8\u4e2d\u5fc3\u3001\u8de8\u626b\u63cf\u4eea\u3001\u8de8\u6a21\u6001 calibration \u672a\u9a8c\u8bc1\u3002<\/p>\n<h4>8. \u4e0e\u4e3b\u6d41\u533b\u5b66\u56fe\u50cf\u5206\u5272\u6846\u67b6\u7684\u5173\u7cfb<\/h4>\n<p>TCSeg \u662f\u8bad\u7ec3\u7b56\u7565\u4e0e\u53ef\u9760\u6027\u5efa\u6a21\u6846\u67b6\uff0c\u53ef\u63a5\u5728 VNet\/3D U-Net\/nnU-Net\u3001MedNeXt\u3001UNetR\/Swin-UNet\/TransUNet\u3001SegMamba\/DAMamba \u7b49 backbone \u4e0a\uff0c\u4f46 dual decoder\u3001prototype feature \u5c42\u548c disagreement \u8bbe\u8ba1\u9700\u8981\u9002\u914d\u3002\u5b83\u4e0d\u662f foundation model\uff0c\u4f46\u201c\u4e0d\u8981\u76f2\u4fe1\u9ad8 confidence\u201d\u548c\u201c\u4e0d\u8981\u53ea\u62a5 best checkpoint\u201d\u7684\u89c2\u70b9\u5bf9 MedSAM\/SAM adaptation \u540c\u6837\u91cd\u8981\u3002<\/p>\n<h4>9. \u5bf9\u6211\u8bfe\u9898\u7684\u4ef7\u503c<\/h4>\n<p>\u5bf9\u534a\u76d1\u7763\/\u5c11\u6807\u6ce8\u533b\u5b66\u5206\u5272\u975e\u5e38\u6709\u4ef7\u503c\uff0c\u53ef\u4f5c\u4e3a reliability-aware SSL \u7684\u91cd\u8981\u53c2\u8003\u3002\u5bf9\u5b9e\u9a8c\u89c4\u8303\u4e5f\u5f88\u6709\u4ef7\u503c\uff1a\u5efa\u8bae\u540e\u7eed\u8bba\u6587\u81f3\u5c11\u62a5\u544a 3\u20135 seeds\u3001best\/last \u6216 mean\u00b1std\uff0c\u907f\u514d single-run best\u3002\u5bf9 DAMamba \u6539\u9020\u6709\u4e2d\u9ad8\u4ef7\u503c\uff1a\u82e5\u505a semi-supervised setting\uff0c\u53ef\u7528 dual-axis pseudo-label filtering \u66ff\u6362\u7b80\u5355 confidence threshold\uff1b\u82e5\u53ea\u505a\u5168\u76d1\u7763 backbone\uff0c\u4e3b\u8981\u4ef7\u503c\u5728 evaluation protocol \u548c related work\u3002\u5bf9 polyp segmentation\uff0c\u53ef\u628a overconfident background near ambiguous boundaries \u4f5c\u4e3a\u6f0f\u68c0\u6765\u6e90\uff0c\u7528 reliability-driven CutMix \u6216 prototype filtering \u5904\u7406\u8fb9\u754c\u3002<\/p>\n<h4>10. \u9605\u8bfb\u5efa\u8bae<\/h4>\n<p><strong>\u5efa\u8bae\u7cbe\u8bfb\u3002<\/strong> \u5982\u679c\u505a\u534a\u76d1\u7763\/\u5c11\u6807\u6ce8\u533b\u5b66\u5206\u5272\uff0c\u5efa\u8bae\u5b8c\u6574\u8bfb Method 3\u3001Evaluation Protocol 4.2\u3001Table 1\u20134 \u548c Limitations\uff1b\u82e5\u53ea\u505a\u5168\u76d1\u7763 backbone\uff0c\u53ef\u7565\u8bfb\u65b9\u6cd5\u7ec6\u8282\uff0c\u91cd\u70b9\u5438\u6536\u201cconfidence \u4e0d\u7b49\u4e8e uncertainty\u201d\u548c\u201cmulti-run best\/last protocol\u201d\u3002<\/p>\n<hr \/>\n<h2>\u4eca\u65e5\u63a8\u8350\u4f18\u5148\u7ea7<\/h2>\n<ol>\n<li><strong>Detail Consistent Stage-Wise Distillation for Efficient 3D MRI Segmentation<\/strong>\uff1a\u6700\u503c\u5f97\u4f18\u5148\u8bfb\u3002\u6a21\u5757\u7b80\u6d01\u3001\u8bad\u7ec3\u671f\u63d2\u62d4\u3001\u5bf9 nnU-Net\/3D U-Net\/DAMamba \u538b\u7f29\u4e0e\u5c0f\u7ed3\u6784\u4fdd\u6301\u6709\u76f4\u63a5\u590d\u73b0\u4ef7\u503c\uff0c\u4e14 MICCAI 2026 \u63a5\u6536\u3001\u6d88\u878d\u8f83\u6e05\u695a\u3002<\/li>\n<li><strong>Are We Overconfident in Models and Results for Semi-Supervised 3D Medical Image Segmentation?<\/strong>\uff1a\u7814\u7a76\u89c4\u8303\u4ef7\u503c\u5f88\u9ad8\u3002\u66f4\u9002\u5408\u534a\u76d1\u7763\/\u5c11\u6807\u6ce8\u65b9\u5411\uff0c\u5c24\u5176\u503c\u5f97\u501f\u9274 reliability decoupling \u4e0e multi-run reporting\uff1b\u82e5\u5f53\u524d\u8bfe\u9898\u662f\u5168\u76d1\u7763 backbone\uff0c\u76f4\u63a5\u5b9e\u7528\u6027\u7565\u4f4e\u4e8e DCD\u3002<\/li>\n<\/ol>\n<h2>\u4eca\u65e5 PDF \u83b7\u53d6\u60c5\u51b5<\/h2>\n<ul>\n<li>\u8bba\u6587 1\uff1a\u5df2\u83b7\u53d6 PDF\uff1b\u672c\u5730\u6587\u4ef6 <code>\/root\/medseg_daily_20260528\/2605.26382.pdf<\/code>\uff1bPDF \u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2605.26382<\/li>\n<li>\u8bba\u6587 2\uff1a\u5df2\u83b7\u53d6 PDF\uff1b\u672c\u5730\u6587\u4ef6 <code>\/root\/medseg_daily_20260528\/2605.25561.pdf<\/code>\uff1bPDF \u94fe\u63a5\uff1ahttps:\/\/arxiv.org\/pdf\/2605.25561<\/li>\n<\/ul>\n<h2>\u4eca\u65e5\u53ef\u6267\u884c\u5efa\u8bae<\/h2>\n<ol>\n<li>\u5148\u590d\u73b0 DCD\uff1a\u628a DCD loss \u52a0\u5230\u8f7b\u91cf 3D U-Net\/nnU-Net \u6216 DAMamba student \u4e0a\uff0c\u91cd\u70b9\u89c2\u5bdf\u5c0f\u75c5\u7076\u3001\u8fb9\u754c\u3001HD95\/NSD\uff1b\u82e5\u505a polyp\uff0c\u53ef\u6539\u6210 2D wavelet detail distillation\u3002<\/li>\n<li>\u534a\u76d1\u7763\u533b\u5b66\u5206\u5272\u5b9e\u9a8c\u5efa\u8bae\u5f15\u5165 TCSeg \u7684\u8bc4\u4f30\u601d\u60f3\uff1a\u81f3\u5c11\u591a seed\uff0c\u533a\u5206 best checkpoint \u4e0e last checkpoint\uff0c\u907f\u514d\u53ea\u62a5\u544a\u5355\u6b21\u6700\u4f18\u7ed3\u679c\u3002<\/li>\n<li>related work \u4e2d\u53ef\u5c06 DCD \u653e\u5165 efficient\/KD\/frequency-domain medical segmentation\uff0c\u5c06 TCSeg \u653e\u5165 reliability-aware semi-supervised medical segmentation \u4e0e evaluation protocol critique\u3002<\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"<p>\u4eca\u65e5\u533b\u5b66\u56fe\u50cf\u5206\u5272\u6700\u65b0\u8bba\u6587\u7cbe\u8bfb\u8ffd\u8e2a \u4eca\u65e5\u7ed3\u8bba \u4eca\u5929\u7b5b\u5230\u7684\u4e24\u7bc7\u6700\u503c\u5f97\u5173\u6ce8\u8bba\u6587\u90fd\u6765\u81ea 2026 \u5e74 5 \u6708\u4e0b\u65ec arXiv\uff1a\u4e00\u7bc7\u662f MI &#8230;<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"emotion":"","emotion_color":"","title_style":"","license":"","footnotes":""},"categories":[85],"tags":[],"class_list":["post-1080","post","type-post","status-publish","format-standard","hentry","category-85"],"views":10,"_links":{"self":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts\/1080","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/comments?post=1080"}],"version-history":[{"count":0,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/posts\/1080\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/media?parent=1080"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/categories?post=1080"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.eutaboo.com\/index.php\/wp-json\/wp\/v2\/tags?post=1080"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}