{"id":36385,"date":"2025-01-10T12:00:10","date_gmt":"2025-01-10T04:00:10","guid":{"rendered":"https:\/\/17aitech.com\/?p=36385"},"modified":"2025-01-10T12:00:10","modified_gmt":"2025-01-10T04:00:10","slug":"tmi2024-%e9%98%bf%e5%a4%a7%e3%80%81%e5%90%8c%e6%b5%8e%e7%ad%89%e6%8f%90%e5%87%batracoco%ef%bc%8c%e7%94%a8%e4%ba%8e3d%e5%8c%bb%e5%ad%a6%e5%9b%be%e5%83%8f%e5%8d%8a%e7%9b%91%e7%9d%a3%e5%88%86%e5%89%b2","status":"publish","type":"post","link":"https:\/\/17aitech.com\/?p=36385","title":{"rendered":"TMI2024 | \u963f\u5927\u3001\u540c\u6d4e\u7b49\u63d0\u51faTraCoCo\uff0c\u7528\u4e8e3D\u533b\u5b66\u56fe\u50cf\u534a\u76d1\u7763\u5206\u5272"},"content":{"rendered":"

\u6587\u7ae0\u6765\u6e90\u4e8e\u4e92\u8054\u7f51:TMI2024 | \u963f\u5927\u3001\u540c\u6d4e\u7b49\u63d0\u51faTraCoCo\uff0c\u7528\u4e8e3D\u533b\u5b66\u56fe\u50cf\u534a\u76d1\u7763\u5206\u5272<\/a><\/p>\n

<\/a><\/p>\n

\u7f16\u8f91 | ScienceAI<\/p>\n

3D \u533b\u5b66\u56fe\u50cf\u5206\u5272\u65b9\u6cd5\u5df2\u7ecf\u53d6\u5f97\u4e86\u6210\u529f\uff0c\u4f46\u5b83\u4eec\u5bf9\u5927\u91cf\u4f53\u7d20\u7ea7\u6807\u6ce8\u6570\u636e\u7684\u4f9d\u8d56\u662f\u4e00\u4e2a\u9700\u8981\u89e3\u51b3\u7684\u7f3a\u70b9\uff0c\u56e0\u4e3a\u83b7\u53d6\u8fd9\u4e9b\u6807\u6ce8\u7684\u6210\u672c\u5f88\u9ad8\u3002<\/p>\n

\u534a\u76d1\u7763\u5b66\u4e60\uff08SSL\uff09\u901a\u8fc7\u4f7f\u7528\u5927\u91cf\u672a\u6807\u6ce8\u6570\u636e\u548c\u5c11\u91cf\u6807\u6ce8\u6570\u636e\u8fdb\u884c\u6a21\u578b\u8bad\u7ec3\uff0c\u89e3\u51b3\u4e86\u8fd9\u4e00\u95ee\u9898\u3002<\/p>\n

\u6700\u6210\u529f\u7684 SSL \u65b9\u6cd5\u57fa\u4e8e\u4e00\u81f4\u6027\u5b66\u4e60\uff0c\u5373\u901a\u8fc7\u6700\u5c0f\u5316\u4ece\u6270\u52a8\u89c6\u56fe\u4e2d\u83b7\u5f97\u7684\u6a21\u578b\u54cd\u5e94\u4e4b\u95f4\u7684\u8ddd\u79bb\u6765\u5b9e\u73b0\u7684\u3002\u8fd9\u4e9b\u6270\u52a8\u901a\u5e38\u5728\u89c6\u56fe\u4e4b\u95f4\u4fdd\u6301\u7a7a\u95f4\u8f93\u5165\u4e0a\u4e0b\u6587\u7684\u4e00\u81f4\u6027\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u4ece\u7a7a\u95f4\u8f93\u5165\u4e0a\u4e0b\u6587\u4e2d\u5b66\u4e60\u5206\u5272\u6a21\u5f0f\uff0c\u800c\u4e0d\u662f\u4ece\u524d\u666f\u5bf9\u8c61\u4e2d\u5b66\u4e60\u3002<\/p>\n

\u5728\u6700\u65b0\u7684\u7814\u7a76\u4e2d\uff0c\u6fb3\u5927\u5229\u4e9a\u963f\u5fb7\u83b1\u5fb7\u5927\u5b66\uff08The University of Adelaide\uff09\u3001\u540c\u6d4e\u5927\u5b66\u7b49\u673a\u6784\u7684\u7814\u7a76\u4eba\u5458\u63d0\u51fa\u4e86 \u00a0TraCoCo\uff0c\u8fd9\u662f\u4e00\u79cd\u4e00\u81f4\u6027\u5b66\u4e60 SSL \u65b9\u6cd5\uff0c\u5b83\u901a\u8fc7\u6539\u53d8\u8f93\u5165\u6570\u636e\u89c6\u56fe\u7684\u7a7a\u95f4\u8f93\u5165\u4e0a\u4e0b\u6587\u6765\u8fdb\u884c\u6270\u52a8\uff0c\u4f7f\u6a21\u578b\u80fd\u591f\u4ece\u524d\u666f\u5bf9\u8c61\u4e2d\u5b66\u4e60\u5206\u5272\u6a21\u5f0f\u3002<\/p>\n

\u6b64\u5916\uff0c\u8be5\u56e2\u961f\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684\u201cConfident Regional Cross entropy (CRC)\u201d\u635f\u5931\uff0c\u8be5\u635f\u5931\u63d0\u9ad8\u4e86\u8bad\u7ec3\u6536\u655b\u6027\uff0c\u5e76\u4fdd\u6301\u4e86\u5bf9\u5171\u8bad\u7ec3\u4f2a\u6807\u7b7e\u9519\u8bef\u7684\u9c81\u68d2\u6027\u3002\u8be5\u65b9\u6cd5\u5728\u591a\u4e2a3D\u6570\u636e\u57fa\u51c6\u4e0a\u8fbe\u5230\u4e86\u6700\u5148\u8fdb\u7684\u7ed3\u679c\uff0c\u4f8b\u5982\u5de6\u5fc3\u623f\uff08LA\uff09\u3001\u80f0\u817aCT\uff08Pancreas\uff09\u3001\u548c\u8111\u80bf\u7624\u5206\u5272\uff08BraTS19\uff09\u3002\u8be5\u65b9\u6cd5\u5728\u4e00\u4e2a 2D \u5207\u7247\u57fa\u51c6\u2014\u2014\u81ea\u52a8\u5fc3\u810f\u8bca\u65ad\u6311\u6218\uff08ACDC\uff09\u4e0a\u4e5f\u53d6\u5f97\u4e86\u6700\u4f73\u7ed3\u679c\uff0c\u8fdb\u4e00\u6b65\u8bc1\u660e\u4e86\u5176\u6709\u6548\u6027\u3002<\/p>\n

<\/a><\/p>\n

\u6587\u7ae0\u5730\u5740\uff1ahttps:\/\/ieeexplore.ieee.org\/abstract\/document\/10695462<\/em><\/p>\n

\u4ee3\u7801\u5730\u5740\uff1ahttps:\/\/github.com\/yyliu01\/TraCoCo<\/em><\/p>\n

\u80cc\u666f<\/strong><\/p>\n

<\/a><\/p>\n

3D\u533b\u5b66\u56fe\u50cf\u5206\u5272\u65b9\u6cd5\u867d\u7136\u5df2\u53d6\u5f97\u6210\u529f\uff0c\u4f46\u5b83\u4eec\u4f9d\u8d56\u4e8e\u5927\u91cf\u4f53\u7d20\u7ea7\u522b\u6807\u6ce8\u6570\u636e\uff0c\u8fd9\u662f\u4e00\u4e2a\u4e9f\u9700\u89e3\u51b3\u7684\u95ee\u9898\uff0c\u56e0\u4e3a\u83b7\u5f97\u8fd9\u4e9b\u6807\u6ce8\u7684\u6210\u672c\u975e\u5e38\u9ad8\u3002\u4e3a\u4e86\u514b\u670d\u8fd9\u4e00\u6311\u6218\uff0c\u534a\u76d1\u7763\u5b66\u4e60\uff08SSL\uff09\u901a\u8fc7\u7ed3\u5408\u5927\u91cf\u672a\u6807\u6ce8\u6570\u636e\u548c\u5c11\u91cf\u6807\u6ce8\u6570\u636e\u6765\u8bad\u7ec3\u6a21\u578b\uff0c\u4ece\u800c\u51cf\u5c11\u5bf9\u6807\u6ce8\u6570\u636e\u7684\u9700\u6c42\u3002<\/p>\n

\u6700\u6210\u529f\u7684 SSL \u65b9\u6cd5\u57fa\u4e8e\u4e00\u81f4\u6027\u5b66\u4e60\uff0c\u901a\u8fc7\u6700\u5c0f\u5316\u6a21\u578b\u5728\u672a\u6807\u6ce8\u6570\u636e\u7684\u4e0d\u540c\u6270\u52a8\u89c6\u56fe\u4e0b\u7684\u54cd\u5e94\u5dee\u5f02\u6765\u5b9e\u73b0\u4e00\u81f4\u6027\u3002\u7136\u800c\uff0c\u8fd9\u4e9b\u6270\u52a8\u901a\u5e38\u4fdd\u6301\u89c6\u56fe\u4e4b\u95f4\u7684\u7a7a\u95f4\u8f93\u5165\u4e0a\u4e0b\u6587\u8f83\u4e3a\u4e00\u81f4\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u6a21\u578b\u4ece\u7a7a\u95f4\u4e0a\u4e0b\u6587\u4e2d\u5b66\u4e60\u5206\u5272\u6a21\u5f0f\uff0c\u800c\u4e0d\u662f\u4ece\u524d\u666f\u5bf9\u8c61\u4e2d\u5b66\u4e60\u3002<\/p>\n

\u4e3a\u89e3\u51b3\u8fd9\u4e00\u95ee\u9898\uff0c\u7814\u7a76\u4eba\u5458\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684 SSL \u65b9\u6cd5\uff0c\u79f0\u4e3a Translation Consistent Co-training\uff08TraCoCo\uff09\u3002TraCoCo \u901a\u8fc7\u6539\u53d8\u8f93\u5165\u6570\u636e\u89c6\u56fe\u7684\u7a7a\u95f4\u8f93\u5165\u4e0a\u4e0b\u6587\u6765\u6270\u52a8\u89c6\u56fe\uff0c\u4f7f\u6a21\u578b\u80fd\u591f\u4ece\u524d\u666f\u5bf9\u8c61\u4e2d\u5b66\u4e60\u5206\u5272\u6a21\u5f0f\u3002<\/p>\n

\u540c\u65f6\uff0c\u8be5\u7814\u7a76\u8fd8\u63d0\u51fa\u4e86\u4e00\u79cd\u65b0\u7684 Confident Regional Cross-Entropy\uff08CRC\uff09\u635f\u5931\uff0c\u8be5\u635f\u5931\u65e8\u5728\u63d0\u9ad8\u8bad\u7ec3\u7684\u6536\u655b\u6027\u5e76\u4fdd\u6301\u5bf9\u5171\u8bad\u7ec3\u4f2a\u6807\u7b7e\u9519\u8bef\u7684\u9c81\u68d2\u6027\u3002<\/p>\n

<\/a><\/p>\n

Translation Consistent Co-training
\n(TraCoCo)<\/strong><\/p>\n

TraCoCo\u662f\u4e00\u79cd\u57fa\u4e8e\u4e92\u52a9\u5b66\u4e60\uff08Co-training\uff09\u6846\u67b6\u7684\u65b9\u6cd5\uff0c\u8be5\u6846\u67b6\u901a\u8fc7\u7ffb\u8bd1\u4e00\u81f4\u6027\uff08Translation Consistency\uff09\u6765\u6270\u52a8\u8f93\u5165\u6570\u636e\u7684\u7a7a\u95f4\u4e0a\u4e0b\u6587\uff0c\u4ece\u800c\u51cf\u5c11\u6a21\u578b\u5bf9\u80cc\u666f\u6a21\u5f0f\u7684\u201c\u8bb0\u5fc6\u201d\uff0c\u786e\u4fdd\u6a21\u578b\u4e13\u6ce8\u4e8e\u524d\u666f\u5bf9\u8c61\u7684\u5206\u5272\u3002<\/p>\n

\u6a21\u578b\u6846\u67b6<\/strong><\/p>\n

TraCoCo \u5305\u542b\u4e24\u4e2a\u521d\u59cb\u53c2\u6570\u4e0d\u540c\u7684\u72ec\u7acb\u7f51\u7edc\uff08\u901a\u5e38\u4e3a VNet\u6216 3D-UNet\uff09\u3002\u8f93\u5165\u6570\u636e\u901a\u8fc7\u968f\u673a\u88c1\u526a\u751f\u6210\u4e24\u4e2a\u5b50\u4f53\u79ef\uff08sub-volumes\uff09\uff0c\u8fd9\u4e24\u4e2a\u5b50\u4f53\u79ef\u7684\u7a7a\u95f4\u4e0a\u4e0b\u6587\u4e0d\u540c\uff0c\u4f46\u5728\u4f53\u7d20\u7f51\u683c\u4e2d\u5b58\u5728\u91cd\u53e0\u533a\u57df\u3002<\/p>\n

\u6a21\u578b\u76ee\u6807<\/strong><\/p>\n

\u4e3a\u4e86\u786e\u4fdd\u6a21\u578b\u5728\u4e0d\u540c\u7a7a\u95f4\u4e0a\u4e0b\u6587\u4e0b\u7684\u524d\u666f\u5206\u5272\u7ed3\u679c\u4e00\u81f4\uff0cTraCoCo \u5f15\u5165\u4e86\u591a\u79cd\u635f\u5931\u51fd\u6570\uff0c\u5305\u62ec\u76d1\u7763\u5b66\u4e60\u635f\u5931\u3001\u534a\u76d1\u7763\u5b66\u4e60\u635f\u5931\u548c\u7ffb\u8bd1\u4e00\u81f4\u6027\u635f\u5931\u3002\u672c\u6587\u7684\u65b9\u6cd5\u5b9e\u73b0\u901a\u8fc7\u6700\u5c0f\u5316\u4ee5\u4e0b\u635f\u5931\u51fd\u6570\u6765\u8fdb\u884c\u8bad\u7ec3\uff1a<\/p>\n

<\/a><\/p>\n

\u5176\u4e2d\uff0c\u5206\u522b\u7531\u76d1\u7763\u5b66\u4e60\u635f\u5931\uff0c\u534a\u76d1\u7763\u5b66\u4e60\u635f\u5931\uff0c\u548c\u7ffb\u8bd1\u4e00\u81f4\u6027\u635f\u5931\u7ec4\u6210\u3002<\/p>\n

\u76d1\u7763\u5b66\u4e60\u635f\u5931\uff08Supervised
\nLearning Loss\uff09<\/strong><\/p>\n

\u76d1\u7763\u5b66\u4e60\u635f\u5931\u901a\u8fc7\u5e26\u6807\u6ce8\u7684\u6570\u636e\u96c6 D_L \u8fdb\u884c\u8ba1\u7b97\uff0c\u5305\u62ec\u4f53\u7d20\u7ea7\u4ea4\u53c9\u71b5\u635f\u5931\uff08Cross-Entropy Loss\uff09\u548c Dice\u635f\u5931\uff0c\u7528\u6765\u4f18\u5316\u6a21\u578b\u7684\u5206\u5272\u6027\u80fd\u3002\u635f\u5931\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

<\/a><\/p>\n

Translation
\nConsistency Loss<\/strong><\/p>\n

Translation Consistency
\nLoss \u4e3b\u8981\u76ee\u6807\u662f\u786e\u4fdd\u6a21\u578b\u5728\u4e0d\u540c\u7a7a\u95f4\u4e0a\u4e0b\u6587\u4e0b\u7684\u5206\u5272\u7ed3\u679c\u4e00\u81f4\u3002\u5177\u4f53\u5b9e\u73b0\u8fc7\u7a0b\u5982\u4e0b\uff1a\u4ece\u8bad\u7ec3\u4f53\u79ef\u4e2d\u968f\u673a\u63d0\u53d6\u4e24\u4e2a\u4e2d\u5fc3\u4e0d\u540c\u7684\u5b50\u4f53\u79ef<\/p>\n

\u635f\u5931\u51fd\u6570\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

<\/a><\/p>\n

<\/a><\/section>\n
<\/a><\/section>\n

\u5176\u4e2d\uff0c\u516c\u5f0f 4 \u662f Kullback-Leibler\uff08KL\uff09\u6563\u5ea6\uff0c\u7528\u4e8e\u8ba1\u7b97\u4e24\u4e2a\u5b50\u4f53\u79ef\u5728\u91cd\u53e0\u533a\u57df\u7684\u5206\u5272\u7ed3\u679c\u4e4b\u95f4\u7684\u5dee\u5f02\uff1b\u516c\u5f0f 5 \u662f\u57fa\u4e8e\u8d1f\u71b5\u7684\u6b63\u5219\u5316\u635f\u5931\uff0c\u7528\u4e8e\u5e73\u8861\u8bad\u7ec3\u4f53\u7d20\u4e2d\u7684\u524d\u666f\u548c\u80cc\u666f\u7c7b\u3002<\/p>\n

\u534a\u76d1\u7763\u5b66\u4e60\u635f\u5931\uff08Semi-supervised
\nLearning Loss\uff09<\/strong><\/p>\n

\u534a\u76d1\u7763\u5b66\u4e60\u635f\u5931\u7528\u4e8e\u5f3a\u5316\u4e24\u4e2a\u6a21\u578b\u4e4b\u95f4\u7684\u5206\u5272\u4e00\u81f4\u6027\uff0cConfident Regional Cross-Entropy (CRC) Loss, \u5176\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

<\/a><\/p>\n

<\/a><\/p>\n

3D
\nCutMix<\/strong><\/p>\n

\u4e3a\u4e86\u8fdb\u4e00\u6b65\u63d0\u9ad8\u8bad\u7ec3\u7684\u6cdb\u5316\u6027\uff0c\u672c\u6587\u91c7\u7528\u4e86 3D
\nCutMix \u6280\u672f\u3002\u5b9e\u73b0\u4e3a\u968f\u673a\u751f\u6210\u4e00\u4e2a 3D \u4e8c\u503c\u63a9\u7801\uff0c\u8be5\u63a9\u7801\u5305\u542b\u4e00\u4e2a\u968f\u673a\u5b9a\u4e49\u4f4d\u7f6e\u548c\u5c3a\u5bf8\u7684\u201c1\u201d\u65b9\u5757\u3002\u63a9\u7801\u5e94\u7528\u5728\u672a\u6807\u6ce8\u6570\u636e\u548c\u4f2a\u6807\u7b7e\u4e0a\uff0c\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

<\/a><\/p>\n

\u5b9e\u9a8c<\/strong><\/p>\n

\u672c\u6587\u5728\u56db\u4e2a\u516c\u5f00\u7684 3D \u533b\u5b66\u56fe\u50cf\u534a\u76d1\u7763\u5206\u5272\u6570\u636e\u96c6\u4e0a\u8fdb\u884c\u4e86\u5b9e\u9a8c\uff0c\u5305\u62ec\uff1a<\/p>\n

\u5de6\u5fc3\u623f\uff08Left Atrium, LA\uff09\uff1a<\/strong>100 \u4e2a 3D MRI \u4f53\u79ef\uff0c\u5176\u4e2d 80 \u4e2a\u7528\u4e8e\u8bad\u7ec3\uff0c20 \u4e2a\u7528\u4e8e\u6d4b\u8bd5\u3002<\/p>\n

\u80f0\u817a CT\uff08Pancreas-CT\uff09\uff1a<\/strong>82 \u4e2a\u5bf9\u6bd4\u589e\u5f3a\u7684 CT \u626b\u63cf\uff0c\u91c7\u7528\u7279\u5b9a\u7684\u9884\u5904\u7406\u6b65\u9aa4\u3002<\/p>\n

\u8111\u80bf\u7624\u5206\u5272 2019\uff08BraTS19\uff09\uff1a<\/strong>\u5305\u542b 335 \u4e2a\u8111\u90e8 MRI \u6837\u672c\uff0c\u6bcf\u4e2a\u6837\u672c\u5305\u542b\u56db\u79cd\u626b\u63cf\u7c7b\u578b\uff08T1, T1-ce, T2, FLAIR\uff09\u3002<\/p>\n

\u81ea\u52a8\u5fc3\u810f\u8bca\u65ad\u6311\u6218\uff08ACDC\uff09\uff1a<\/strong>\u8be5\u6570\u636e\u96c6\u5305\u542b 100 \u4e2a\u5fc3\u810f MRI \u626b\u63cf\uff0c\u672c\u6587\u4f7f\u7528\u4e86\u5176 2D \u5207\u7247\u8fdb\u884c\u534a\u76d1\u7763\u5b66\u4e60\u3002<\/p>\n

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