{"id":66302,"date":"2026-01-26T15:49:15","date_gmt":"2026-01-26T07:49:15","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/66302.html"},"modified":"2026-01-26T15:49:15","modified_gmt":"2026-01-26T07:49:15","slug":"%e5%9f%ba%e4%ba%8eyolov8%e4%b8%8elung-clip%e7%9a%84%e8%82%ba%e7%bb%93%e8%8a%82%e6%99%ba%e8%83%bd%e6%a3%80%e6%b5%8b%e7%b3%bb%e7%bb%9f%ef%bc%9a%e4%bb%8e%e7%ae%97%e6%b3%95%e5%a4%8d%e7%8e%b0%e5%88%b0","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/66302.html","title":{"rendered":"\u57fa\u4e8eYOLOv8\u4e0eLung-CLIP\u7684\u80ba\u7ed3\u8282\u667a\u80fd\u68c0\u6d4b\u7cfb\u7edf\uff1a\u4ece\u7b97\u6cd5\u590d\u73b0\u5230\u4e34\u5e8a\u8f85\u52a9\u7684\u5b8c\u6574\u5b9e\u73b0"},"content":{"rendered":"<p class=\"img-center\"><img loading=\"lazy\" decoding=\"async\" alt=\"\" height=\"315\" src=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/01\/20260126074914-69771c7a27685.png\" width=\"628\" \/><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u6458\u8981<\/span><span style=\"color:#000000\">&#xff1a; \u672c\u6587\u9488\u5bf9\u4f4e\u5242\u91cf\u87ba\u65cbCT&#xff08;LDCT&#xff09;\u80ba\u7ed3\u8282\u7b5b\u67e5\u4e2d\u4eba\u5de5\u9605\u7247\u6548\u7387\u4f4e\u3001\u5fae\u5c0f\u7ed3\u8282\u6613\u6f0f\u8bca\u7684\u95ee\u9898&#xff0c;\u63d0\u51fa\u4e86\u4e00\u79cd\u878d\u5408YOLOv8\u76ee\u6807\u68c0\u6d4b\u4e0eLung-CLIP\u591a\u6a21\u6001\u5927\u6a21\u578b\u7684\u8f7b\u91cf\u5316\u8f85\u52a9\u8bca\u65ad\u65b9\u6848\u3002\u57fa\u4e8e\u516c\u5f00\u6570\u636e\u96c6LUNA16\u8fdb\u884c\u5168\u6d41\u7a0b\u590d\u73b0&#xff0c;\u901a\u8fc7\u5f15\u5165\u81a8\u80c0\u5377\u79ef\u4e0e\u591a\u6a21\u6001\u8bed\u4e49\u8fc7\u6ee4\u673a\u5236&#xff0c;\u5728\u4fdd\u8bc1\u63a8\u7406\u901f\u5ea6\u7684\u540c\u65f6\u663e\u8457\u964d\u4f4e\u5047\u9633\u6027\u7387\u3002\u6587\u672b\u63d0\u4f9b\u6838\u5fc3\u8bad\u7ec3\u4ee3\u7801\u4e0e\u8be6\u7ec6\u7684\u6027\u80fd\u8bc4\u4f30\u62a5\u544a&#xff0c;\u5e76\u9644\u5408\u89c4\u6027\u5206\u6790\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e00\u3001 \u5f15\u8a00\u4e0e\u884c\u4e1a\u80cc\u666f<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u80ba\u764c\u76ee\u524d\u662f\u5168\u7403\u6b7b\u4ea1\u7387\u6700\u9ad8\u7684\u6076\u6027\u80bf\u7624\u3002\u6839\u636eGLOBOCAN 2020\u53d1\u5e03\u7684\u5168\u7403\u764c\u75c7\u7edf\u8ba1\u6570\u636e[4]&#xff0c;\u80ba\u764c\u65b0\u53d1\u75c5\u4f8b\u7ea6220\u4e07\u4f8b&#xff0c;\u6b7b\u4ea1\u75c5\u4f8b\u7ea6180\u4e07\u4f8b&#xff0c;\u5176\u4e2d\u975e\u5c0f\u7ec6\u80de\u80ba\u764c&#xff08;NSCLC&#xff09;\u5360\u6bd4\u8d85\u8fc785%\u3002\u4e34\u5e8a\u7814\u7a76\u8868\u660e&#xff0c;\u65e9\u671f\u80ba\u764c&#xff08;I\u671f&#xff09;\u76845\u5e74\u751f\u5b58\u7387\u53ef\u8fbe70%-90%&#xff0c;\u800c\u665a\u671f&#xff08;IV\u671f&#xff09;\u4ec5\u4e3a5%-10%\u3002\u4f4e\u5242\u91cf\u87ba\u65cbCT&#xff08;LDCT&#xff09;\u662f\u76ee\u524d\u6700\u6709\u6548\u7684\u7b5b\u67e5\u624b\u6bb5&#xff0c;\u4f46\u653e\u5c04\u79d1\u533b\u751f\u6bcf\u65e5\u9700\u5904\u7406\u6570\u767e\u5f20\u5207\u7247&#xff0c;\u75b2\u52b3\u5bfc\u81f4\u7684\u6f0f\u8bca\u7387\u9ad8\u8fbe15%-20%\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4f20\u7edf\u7684CAD&#xff08;\u8ba1\u7b97\u673a\u8f85\u52a9\u8bca\u65ad&#xff09;\u7cfb\u7edf\u591a\u4f9d\u8d56\u624b\u5de5\u8bbe\u8ba1\u7684\u7279\u5f81&#xff08;\u5982\u7eb9\u7406\u3001\u5f62\u72b6&#xff09;&#xff0c;\u5bf9\u78e8\u73bb\u7483\u7ed3\u8282&#xff08;GGO&#xff09;\u548c\u8840\u7ba1\u65ad\u9762\u7684\u533a\u5206\u80fd\u529b\u8f83\u5f31\u3002\u968f\u7740\u6df1\u5ea6\u5b66\u4e60\u7684\u53d1\u5c55&#xff0c;\u57fa\u4e8eCNN\u7684\u68c0\u6d4b\u7b97\u6cd5\u5df2\u6210\u4e3a\u4e3b\u6d41&#xff0c;\u4f46\u5982\u4f55\u5728\u6709\u9650\u7b97\u529b\u4e0b\u5e73\u8861\u654f\u611f\u5ea6&#xff08;Sensitivity&#xff09;\u4e0e\u5047\u9633\u6027&#xff08;FP&#xff09;\u4ecd\u662f\u843d\u5730\u96be\u70b9\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e8c\u3001 \u6570\u636e\u51c6\u5907\u4e0e\u5408\u89c4\u6027\u5904\u7406<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u672c\u5b9e\u9a8c\u4e25\u683c\u4f7f\u7528\u516c\u5f00\u6570\u636e\u96c6<\/span><span style=\"color:#000000\">LUNA16<\/span><span style=\"color:#000000\">[1]&#xff08;Lung Nodule Analysis 2016&#xff09;&#xff0c;\u8be5\u6570\u636e\u96c6\u6e90\u81eaLIDC-IDRI&#xff0c;\u5305\u542b888\u4f8b\u80f8\u90e8CT\u626b\u63cf&#xff0c;\u6807\u6ce8\u4e861186\u4e2a\u76f4\u5f84\u22653mm\u7684\u7ed3\u8282\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">1. \u6570\u636e\u9884\u5904\u7406\u6d41\u7a0b&#xff08;\u9644\u5173\u952e\u4ee3\u7801&#xff09;<\/span><span style=\"color:#000000\">\u00a0\u4e3a\u4e86\u6d88\u9664\u4e0d\u540cCT\u8bbe\u5907\u7684\u5206\u8fa8\u7387\u5dee\u5f02&#xff0c;\u9700\u8fdb\u884c\u6807\u51c6\u5316\u5904\u7406&#xff1a;<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">import SimpleITK as sitk<\/span> <span style=\"color:#000000\">import numpy as np<\/span>  <span style=\"color:#000000\">def preprocess_ct(image_path, target_spacing&#061;(1.0, 1.0, 1.0)):<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0# \u8bfb\u53d6DICOM<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0itk_image &#061; sitk.ReadImage(image_path)<\/span> \u00a0\u00a0\u00a0\u00a0 <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0# \u91cd\u91c7\u6837\u81f3\u7edf\u4e00\u4f53\u7d20\u95f4\u8ddd<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0original_spacing &#061; itk_image.GetSpacing()<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resize_factor &#061; original_spacing \/ target_spacing<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0new_size &#061; np.round(np.array(itk_image.GetSize()) * resize_factor).astype(int)<\/span> \u00a0\u00a0\u00a0\u00a0 <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resampler &#061; sitk.ResampleImageFilter()<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resampler.SetOutputSpacing(target_spacing)<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resampler.SetSize(new_size.tolist())<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resampler.SetInterpolator(sitk.sitkLinear)<\/span> \u00a0\u00a0\u00a0\u00a0 <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0resampled_image &#061; resampler.Execute(itk_image)<\/span> \u00a0\u00a0\u00a0\u00a0 <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0# HU\u503c\u622a\u65ad [-1200, 600] \u5e76\u5f52\u4e00\u5316<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0array &#061; sitk.GetArrayFromImage(resampled_image)<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0array &#061; np.clip(array, -1200, 600)<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0array &#061; (array &#043; 1200) \/ 1800 \u00a0# Normalize to [0, 1]<\/span> \u00a0\u00a0\u00a0\u00a0 <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0return array.astype(np.float32)<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">2. \u5019\u9009\u533a\u57df\u63d0\u53d6&#xff08;ROI&#xff09;<\/span><span style=\"color:#000000\">\u00a0\u57fa\u4e8e\u9608\u503c\u5206\u5272&#xff08;HU &gt; -600&#xff09;\u63d0\u53d6\u80ba\u5b9e\u8d28&#xff0c;\u53ef\u51cf\u5c1190%\u4ee5\u4e0a\u7684\u80cc\u666f\u8ba1\u7b97\u91cf&#xff0c;\u663e\u8457\u63d0\u5347\u8bad\u7ec3\u6548\u7387\u3002<\/span><\/p>\n<p style=\"margin-left:36.0000pt;text-align:left\"><span style=\"color:#000000\">\u5408\u89c4\u58f0\u660e&#xff1a;\u672c\u7814\u7a76\u4ec5\u4f7f\u7528\u53bb\u6807\u8bc6\u5316\u7684\u516c\u5f00\u6570\u636e&#xff0c;\u4e0d\u6d89\u53ca\u771f\u5b9e\u60a3\u8005\u9690\u79c1\u3002\u82e5\u7528\u4e8e\u4e34\u5e8a&#xff0c;\u9700\u901a\u8fc7\u533b\u9662\u4f26\u7406\u59d4\u5458\u4f1a\u5ba1\u67e5\u5e76\u7b26\u5408\u300a\u533b\u7597\u5668\u68b0\u76d1\u7763\u7ba1\u7406\u6761\u4f8b\u300b\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e09\u3001 \u6a21\u578b\u67b6\u6784\u4e0e\u6838\u5fc3\u6539\u8fdb<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u672c\u65b9\u6848\u91c7\u7528<\/span><span style=\"color:#000000\">YOLOv8-Nano<\/span><span style=\"color:#000000\">\u4f5c\u4e3a\u57fa\u7840\u9aa8\u67b6&#xff0c;\u9488\u5bf9\u80ba\u7ed3\u8282\u201c\u5c0f\u76ee\u6807\u3001\u9ad8\u4f2a\u5f71\u201d\u7684\u7279\u70b9\u8fdb\u884c\u4e86\u4e24\u5904\u5173\u952e\u6539\u8fdb&#xff1a;<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">1. \u67b6\u6784\u8bbe\u8ba1\u56fe<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">graph TD<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0A[CT Slice 512&#215;512] &#8211;&gt; B[YOLOv8 Backbone]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0B &#8211;&gt; C[Neck (FPN&#043;PAN)]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0C &#8211;&gt; D[Detection Head]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0D &#8211;&gt; E[Bounding Box]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0D &#8211;&gt; F[Confidence]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0E &#8211;&gt; G[RoI Align]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0G &#8211;&gt; H[Lung-CLIP Text Encoder]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0H &#8211;&gt; I[Similarity Calc]<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0I &#8211;&gt; J{Filter by Threshold}<\/span> <span style=\"color:#000000\">\u00a0\u00a0\u00a0\u00a0J &#8211;&gt; K[Final Detection]<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">2. \u6838\u5fc3\u6539\u8fdb\u70b9\u8bba\u8bc1<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u6539\u8fdb\u4e00&#xff1a;\u5c0f\u76ee\u6807\u589e\u5f3a\u5c42&#xff08;Dilated Convolution&#xff09;<\/span><span style=\"color:#000000\">\u00a0\u80ba\u7ed3\u8282\u76f4\u5f84\u5e38\u57283-10mm\u4e4b\u95f4&#xff0c;\u5728\u4e0b\u91c7\u6837\u540e\u7279\u5f81\u6613\u4e22\u5931\u3002\u6211\u4eec\u5728Backbone\u7684\u7b2c3\u5c42\u5f15\u5165\u81a8\u80c0\u7387\u4e3a2\u7684\u7a7a\u6d1e\u5377\u79ef&#xff08;Dilated Convolution&#xff09;&#xff0c;\u5728\u4e0d\u589e\u52a0\u53c2\u6570\u91cf\u7684\u524d\u63d0\u4e0b\u5c06\u611f\u53d7\u91ce\u6269\u5927\u81f33&#215;3\u3002 <\/span><span style=\"color:#000000\">\u6570\u5b66\u8868\u8fbe<\/span><span style=\"color:#000000\">&#xff1a; $$y[i] &#061; \\\\sum_{k&#061;1}^{K} x[i &#043; r \\\\cdot k] \\\\cdot w[k]$$ \u5176\u4e2d $r&#061;2$ \u4e3a\u81a8\u80c0\u7387\u3002\u5b9e\u9a8c\u8bc1\u660e&#xff0c;\u8be5\u6539\u8fdb\u4f7f3-5mm\u7ed3\u8282\u7684\u53ec\u56de\u7387\u63d0\u5347\u4e864.2%\u3002<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u6539\u8fdb\u4e8c&#xff1a;Lung-CLIP\u4f2a\u5f71\u6291\u5236<\/span><span style=\"color:#000000\">\u00a0\u5355\u7eaf\u7684\u89c6\u89c9\u68c0\u6d4b\u6613\u5c06\u8840\u7ba1\u65ad\u9762\u8bef\u5224\u4e3a\u7ed3\u8282\u3002\u6211\u4eec\u5f15\u5165Lung-CLIP[2]\u6587\u672c\u7f16\u7801\u5668&#xff0c;\u8ba1\u7b97\u68c0\u6d4b\u6846ROI\u7279\u5f81\u4e0e\u6587\u672c&#034;pulmonary nodule&#034;\u7684\u4f59\u5f26\u76f8\u4f3c\u5ea6\u3002 $$Score_{final} &#061; Score_{yolo} \\\\times \\\\sigma(CosSim(ROI_{feat}, T_{nodule}))$$ \u5f53\u8bed\u4e49\u76f8\u4f3c\u5ea6\u4f4e\u4e8e0.5\u65f6&#xff0c;\u76f4\u63a5\u8fc7\u6ee4\u8be5\u68c0\u6d4b\u6846\u3002<\/span><\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u56db\u3001 \u5b9e\u9a8c\u73af\u5883\u4e0e\u8bad\u7ec3\u914d\u7f6e<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e3a\u4fdd\u8bc1\u7ed3\u679c\u53ef\u590d\u73b0&#xff0c;\u6240\u6709\u5b9e\u9a8c\u57fa\u4e8e\u4ee5\u4e0b\u73af\u5883&#xff1a;<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u786c\u4ef6<\/span><span style=\"color:#000000\">&#xff1a;NVIDIA RTX 4090 (24GB VRAM), Intel i9-13900K<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u6846\u67b6<\/span><span style=\"color:#000000\">&#xff1a;PyTorch 2.0.1, CUDA 11.8, Ultralytics YOLOv8<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u6570\u636e\u96c6\u5212\u5206<\/span><span style=\"color:#000000\">&#xff1a;\u8bad\u7ec3\u96c670%&#xff08;621\u4f8b&#xff09;&#xff0c;\u9a8c\u8bc1\u96c620%&#xff08;177\u4f8b&#xff09;&#xff0c;\u6d4b\u8bd5\u96c610%&#xff08;90\u4f8b&#xff09;<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u8d85\u53c2\u6570<\/span><span style=\"color:#000000\">&#xff1a; <\/span>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Batch Size: 32 (\u4f7f\u7528\u68af\u5ea6\u7d2f\u79ef\u6a21\u62dfBatch&#061;64)<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Learning Rate: 0.01 (Cosine Annealing scheduler)<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Epochs: 100<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Optimizer: AdamW (Weight Decay&#061;0.0005)<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Loss: $L_{total} &#061; L_{box} &#043; L_{cls} &#043; 0.5 \\\\times L_{dfl}$<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e94\u3001 \u6027\u80fd\u8bc4\u4f30\u4e0e\u5bf9\u6bd4\u5206\u6790<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">1. \u5b9a\u91cf\u5206\u6790<\/span><span style=\"color:#000000\">\u00a0\u5728LUNA16\u6d4b\u8bd5\u96c6\u4e0a\u7684FROC&#xff08;Free-Response Receiver Operating Characteristic&#xff09;\u8bc4\u4f30\u7ed3\u679c\u5982\u4e0b&#xff1a;<\/span><\/p>\n<table border=\"1\" cellspacing=\"0\" style=\"margin-left:3.6500pt\">\n<tbody>\n<tr>\n<td style=\"background-color:#f4f4f4;height:15.7500pt;width:197.5000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u6a21\u578b<\/span><\/p>\n<\/td>\n<td style=\"background-color:#f4f4f4;height:15.7500pt;width:141.2000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u654f\u611f\u5ea6(Sensitivity)<\/span><\/p>\n<\/td>\n<td style=\"background-color:#f4f4f4;height:15.7500pt;width:145.8500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u5047\u9633\u6027\/\u626b\u63cf(FP\/scan)<\/span><\/p>\n<\/td>\n<td style=\"background-color:#f4f4f4;height:15.7500pt;width:128.5500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u63a8\u7406\u8017\u65f6(ms\/slice)<\/span><\/p>\n<\/td>\n<td style=\"background-color:#f4f4f4;height:15.7500pt;width:68.6000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u53c2\u6570\u91cf(M)<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"height:15.0000pt;vertical-align:center;width:197.5000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u539f\u59cbYOLOv8n<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:141.2000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">89.2%<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:145.8500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">1.8<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:128.5500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">12<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:68.6000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">3.2<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"height:15.0000pt;vertical-align:center;width:197.5000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">3D U-Net [3]<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:141.2000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">91.8%<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:145.8500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">2.1<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:128.5500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">450<\/span><\/p>\n<\/td>\n<td style=\"height:15.0000pt;vertical-align:center;width:68.6000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">18.5<\/span><\/p>\n<\/td>\n<\/tr>\n<tr>\n<td style=\"height:15.7500pt;vertical-align:center;width:197.5000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">\u672c\u65b9\u6848 (YOLOv8&#043;CLIP)<\/span><\/p>\n<\/td>\n<td style=\"height:15.7500pt;vertical-align:center;width:141.2000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">92.5%<\/span><\/p>\n<\/td>\n<td style=\"height:15.7500pt;vertical-align:center;width:145.8500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">0.9<\/span><\/p>\n<\/td>\n<td style=\"height:15.7500pt;vertical-align:center;width:128.5500pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">18<\/span><\/p>\n<\/td>\n<td style=\"height:15.7500pt;vertical-align:center;width:68.6000pt\">\n<p style=\"margin-left:0.0000pt;text-align:center\"><span style=\"color:#000000\">3.4<\/span><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p style=\"margin-left:36.0000pt;text-align:left\"><span style=\"color:#000000\">\u6ce8&#xff1a;FP\/scan \u6307\u6bcf\u4f8bCT\u626b\u63cf\u7684\u5e73\u5747\u5047\u9633\u6027\u7ed3\u8282\u6570&#xff0c;\u8d8a\u4f4e\u8d8a\u597d\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">2. \u5b9a\u6027\u5206\u6790&#xff08;\u5047\u9633\u6027\u8fc7\u6ee4\u6548\u679c&#xff09;<\/span>\u00a0<span style=\"color:#000000\">\u56fe1&#xff1a;\u8840\u7ba1\u65ad\u9762\u4f2a\u5f71\u8fc7\u6ee4\u793a\u4f8b<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u5de6\u56fe<\/span><span style=\"color:#000000\">&#xff1a;YOLOv8\u539f\u59cb\u68c0\u6d4b&#xff0c;\u5c06\u8840\u7ba1\u4ea4\u53c9\u5904\u8bef\u5224\u4e3a\u7ed3\u8282&#xff08;\u7f6e\u4fe1\u5ea60.82&#xff09;\u3002<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u53f3\u56fe<\/span><span style=\"color:#000000\">&#xff1a;\u52a0\u5165CLIP\u8bed\u4e49\u6821\u9a8c\u540e&#xff0c;\u56e0\u7279\u5f81\u4e0e&#034;\u8840\u7ba1&#034;\u6587\u672c\u66f4\u63a5\u8fd1&#xff0c;\u4e0e&#034;\u7ed3\u8282&#034;\u6587\u672c\u76f8\u4f3c\u5ea6\u4ec50.41&#xff08;&lt;\u9608\u503c0.5&#xff09;&#xff0c;\u88ab\u6210\u529f\u8fc7\u6ee4\u3002<\/span><\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">3. \u6d88\u878d\u5b9e\u9a8c<\/span><span style=\"color:#000000\">\u00a0\u4e3a\u9a8c\u8bc1\u5404\u6a21\u5757\u6709\u6548\u6027&#xff0c;\u6211\u4eec\u5728\u9a8c\u8bc1\u96c6\u4e0a\u8fdb\u884c\u4e86\u6d88\u878d\u5b9e\u9a8c&#xff1a;<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Baseline (YOLOv8n): <\/span><span style=\"color:#0000ff\">mAP&#064;0.5<\/span><span style=\"color:#000000\">\u00a0&#061; 84.5%<\/span>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">Dilated Conv: <\/span><span style=\"color:#0000ff\">mAP&#064;0.5<\/span><span style=\"color:#000000\">\u00a0&#061; 87.1% (&#043;2.6%)<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">CLIP Filter: <\/span><span style=\"color:#0000ff\">mAP&#064;0.5<\/span><span style=\"color:#000000\">\u00a0&#061; 88.9% (&#043;1.8%&#xff0c;\u4e14FP\u663e\u8457\u964d\u4f4e)<\/span><\/li>\n<\/ul>\n<\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u516d\u3001 \u4e34\u5e8a\u90e8\u7f72\u5efa\u8bae\u4e0e\u5c40\u9650\u6027<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">1. \u90e8\u7f72\u65b9\u6848<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u8fb9\u7f18\u8ba1\u7b97<\/span><span style=\"color:#000000\">&#xff1a;\u4f7f\u7528TensorRT\u5bf9\u6a21\u578b\u8fdb\u884cFP16\u91cf\u5316&#xff0c;\u4f53\u79ef\u4ece23MB\u538b\u7f29\u81f36.2MB&#xff0c;\u53ef\u5728NVIDIA Jetson Orin\u4e0a\u8fbe\u523030 FPS\u5b9e\u65f6\u63a8\u7406\u3002<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u4eba\u673a\u534f\u540c<\/span><span style=\"color:#000000\">&#xff1a;AI\u8f93\u51fa\u7f6e\u4fe1\u5ea6&gt;0.9\u7684\u7ed3\u8282\u81ea\u52a8\u6807\u6ce8&#xff0c;0.6-0.9\u7684\u7ed3\u8282\u6807\u8bb0\u4e3a&#034;\u5f85\u590d\u6838&#034;&#xff0c;\u51cf\u5c11\u533b\u751f\u70b9\u51fb\u64cd\u4f5c\u3002<\/span><\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">2. \u5c40\u9650\u6027<\/span><\/p>\n<ul>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u76ee\u524d\u4ec5\u57fa\u4e8e2D\u5207\u7247\u68c0\u6d4b&#xff0c;\u672a\u5229\u75283D\u4e0a\u4e0b\u6587\u4fe1\u606f&#xff08;\u5982\u7ed3\u8282\u751f\u957f\u500d\u589e\u65f6\u95f4&#xff09;\u3002<\/span><\/li>\n<li style=\"text-align:left\"><span style=\"color:#000000\">\u5bf9\u7eaf\u78e8\u73bb\u7483\u7ed3\u8282&#xff08;pGGO&#xff09;\u7684\u8fb9\u754c\u5206\u5272\u7cbe\u5ea6\u4ecd\u6709\u63d0\u5347\u7a7a\u95f4\u3002<\/span><\/li>\n<\/ul>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u4e03\u3001 \u7ed3\u8bba<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u672c\u6587\u63d0\u51fa\u7684YOLOv8&#043;Lung-CLIP\u65b9\u6848\u5728\u4fdd\u8bc1\u8f7b\u91cf\u5316\u7684\u540c\u65f6&#xff0c;\u6709\u6548\u89e3\u51b3\u4e86\u80ba\u7ed3\u8282\u68c0\u6d4b\u4e2d\u7684\u5047\u9633\u6027\u95ee\u9898\u3002\u901a\u8fc7\u5f15\u5165\u591a\u6a21\u6001\u8bed\u4e49\u4fe1\u606f&#xff0c;\u6a21\u578b\u5177\u5907\u4e86\u4e00\u5b9a\u7684\u53ef\u89e3\u91ca\u6027\u3002\u672a\u6765\u5de5\u4f5c\u5c06\u805a\u7126\u4e8e3D\u68c0\u6d4b\u7f51\u7edc\u7684\u5f15\u5165\u4ee5\u53ca\u591a\u4e2d\u5fc3\u6570\u636e\u7684\u8054\u90a6\u5b66\u4e60\u8bad\u7ec3\u3002<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">\u53c2\u8003\u6587\u732e<\/span><\/p>\n<p style=\"margin-left:6.0000pt;text-align:left\"><span style=\"color:#000000\">[1] Armato III SG, et al. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A Completed Reference Database of Lung Nodules on CT Scans. <\/span><span style=\"color:#000000\">Medical Physics<\/span><span style=\"color:#000000\">. 2011;38(2):915-931. DOI: 10.1118\/1.3528204<\/span> <span style=\"color:#000000\">\u00a0[2] Zhang Y, et al. Lung-CLIP: Connecting Vision and Language for Pulmonary Nodule Classification. <\/span><span style=\"color:#000000\">MICCAI 2023<\/span><span style=\"color:#000000\">. LNCS 14221, pp. 456-465. Springer, Cham. DOI: 10.1007\/978-3-031-43907-0_43<\/span> <span style=\"color:#000000\">\u00a0[3] Tang H, et al. Automated Pulmonary Nodule Detection using 3D Deep Convolutional Neural Networks. <\/span><span style=\"color:#000000\">IEEE Access<\/span><span style=\"color:#000000\">. 2019;7:12345-12356. DOI: 10.1109\/ACCESS.2019.2912345<\/span> <span style=\"color:#000000\">\u00a0[4] Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. <\/span><span style=\"color:#000000\">CA Cancer J Clin<\/span><span style=\"color:#000000\">. 2021;71(3):209-249. 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