{"id":32940,"date":"2025-07-21T06:40:33","date_gmt":"2025-07-20T22:40:33","guid":{"rendered":"https:\/\/www.kdjingpai.com\/?p=32940"},"modified":"2025-07-21T06:40:33","modified_gmt":"2025-07-20T22:40:33","slug":"medgemma","status":"publish","type":"post","link":"https:\/\/www.kdjingpai.com\/en\/medgemma\/","title":{"rendered":"MedGemma\uff1a\u533b\u7597\u6587\u672c\u4e0e\u56fe\u50cf\u7406\u89e3\u7684\u5f00\u6e90AI\u6a21\u578b\u96c6\u5408"},"content":{"rendered":"<p>MedGemma \u662f Google \u5728 Hugging Face \u5e73\u53f0\u4e0a\u53d1\u5e03\u7684\u4e00\u7ec4\u5f00\u6e90 AI \u6a21\u578b\uff0c\u4e13\u6ce8\u4e8e\u533b\u7597\u9886\u57df\u7684\u6587\u672c\u548c\u56fe\u50cf\u7406\u89e3\u3002\u5b83\u57fa\u4e8e <a href=\"https:\/\/www.kdjingpai.com\/gemma-3-jishubaogao\/\">Gemma 3<\/a> \u6a21\u578b\u5f00\u53d1\uff0c\u65e8\u5728\u5e2e\u52a9\u5f00\u53d1\u8005\u6784\u5efa\u533b\u7597\u76f8\u5173\u7684 AI \u5e94\u7528\u3002MedGemma \u63d0\u4f9b\u591a\u79cd\u6a21\u578b\u53d8\u4f53\uff0c\u5305\u62ec 4B \u53c2\u6570\u7684\u591a\u6a21\u6001\u6a21\u578b\u548c 27B \u53c2\u6570\u7684\u6587\u672c\u53ca\u591a\u6a21\u6001\u6a21\u578b\u3002\u8fd9\u4e9b\u6a21\u578b\u5728\u533b\u7597\u6587\u672c\u3001\u7535\u5b50\u5065\u5eb7\u8bb0\u5f55 (EHR) \u548c\u591a\u79cd\u533b\u7597\u5f71\u50cf\uff08\u5982 X \u5149\u3001\u76ae\u80a4\u79d1\u56fe\u50cf\u3001\u773c\u79d1\u56fe\u50cf\u548c\u7ec4\u7ec7\u75c5\u7406\u5207\u7247\uff09\u4e0a\u8fdb\u884c\u4e13\u95e8\u8bad\u7ec3\u3002\u5f00\u53d1\u8005\u53ef\u4ee5\u5229\u7528\u8fd9\u4e9b\u6a21\u578b\u52a0\u901f\u533b\u7597 AI \u5e94\u7528\u5f00\u53d1\uff0c\u4f8b\u5982\u653e\u5c04\u5b66\u62a5\u544a\u751f\u6210\u3001\u533b\u7597\u95ee\u7b54\u548c\u56fe\u50cf\u5206\u7c7b\u7b49\u3002MedGemma \u7684\u5f00\u6e90\u7279\u6027\u4f7f\u5176\u6613\u4e8e\u8bbf\u95ee\uff0c\u9002\u5408\u7814\u7a76\u4eba\u5458\u548c\u5f00\u53d1\u8005\u5728\u5355 GPU \u4e0a\u8fd0\u884c\uff0c\u964d\u4f4e\u5f00\u53d1\u95e8\u69db\u3002<\/p>\n<p><img decoding=\"async\" src=\"https:\/\/www.kdjingpai.com\/wp-content\/uploads\/2025\/07\/1efb3173f48fab5-scaled.png\" alt=\"\" \/><\/p>\n<p>&nbsp;<\/p>\n<h2>\u529f\u80fd\u5217\u8868<\/h2>\n<ul>\n<li>\u533b\u7597\u6587\u672c\u5904\u7406\uff1a\u5206\u6790\u548c\u751f\u6210\u533b\u7597\u76f8\u5173\u7684\u6587\u672c\u5185\u5bb9\uff0c\u5982\u533b\u7597\u62a5\u544a\u3001\u95ee\u7b54\u5bf9\u548c\u7535\u5b50\u5065\u5eb7\u8bb0\u5f55\u3002<\/li>\n<li>\u533b\u7597\u56fe\u50cf\u7406\u89e3\uff1a\u652f\u6301\u591a\u79cd\u533b\u7597\u5f71\u50cf\u5206\u6790\uff0c\u5305\u62ec\u80f8\u90e8 X \u5149\u3001\u76ae\u80a4\u79d1\u56fe\u50cf\u3001\u773c\u79d1\u56fe\u50cf\u548c\u7ec4\u7ec7\u75c5\u7406\u5207\u7247\u3002<\/li>\n<li>\u591a\u6a21\u6001\u63a8\u7406\uff1a\u7ed3\u5408\u6587\u672c\u548c\u56fe\u50cf\u6570\u636e\uff0c\u63d0\u4f9b\u7efc\u5408\u7684\u533b\u7597\u63a8\u7406\u80fd\u529b\uff0c\u5982\u751f\u6210\u653e\u5c04\u5b66\u62a5\u544a\u6216\u89e3\u91ca\u56fe\u50cf\u5185\u5bb9\u3002<\/li>\n<li>\u6a21\u578b\u53d8\u4f53\u9009\u62e9\uff1a\u63d0\u4f9b 4B \u53c2\u6570\u591a\u6a21\u6001\u6a21\u578b\uff08\u9884\u8bad\u7ec3\u548c\u6307\u4ee4\u5fae\u8c03\u7248\u672c\uff09\u53ca 27B \u53c2\u6570\u6587\u672c\u548c\u591a\u6a21\u6001\u6a21\u578b\uff08\u4ec5\u6307\u4ee4\u5fae\u8c03\u7248\u672c\uff09\u3002<\/li>\n<li>\u9ad8\u6548\u63a8\u7406\u4f18\u5316\uff1a\u6a21\u578b\u7ecf\u8fc7\u4f18\u5316\uff0c\u9002\u5408\u5728\u5355 GPU \u4e0a\u8fd0\u884c\uff0c\u964d\u4f4e\u8ba1\u7b97\u8d44\u6e90\u9700\u6c42\u3002<\/li>\n<li>\u5f00\u6e90\u4e0e\u53ef\u5fae\u8c03\uff1a\u6a21\u578b\u5b8c\u5168\u5f00\u6e90\uff0c\u5f00\u53d1\u8005\u53ef\u6839\u636e\u5177\u4f53\u9700\u6c42\u8fdb\u884c\u5fae\u8c03\uff0c\u63d0\u5347\u6027\u80fd\u3002<\/li>\n<\/ul>\n<h2>\u4f7f\u7528\u5e2e\u52a9<\/h2>\n<h3>\u5b89\u88c5\u4e0e\u90e8\u7f72<\/h3>\n<p>MedGemma \u6a21\u578b\u6258\u7ba1\u5728 Hugging Face \u5e73\u53f0\uff0c\u5f00\u53d1\u8005\u65e0\u9700\u590d\u6742\u5b89\u88c5\u5373\u53ef\u4f7f\u7528\u3002\u4ee5\u4e0b\u662f\u5177\u4f53\u64cd\u4f5c\u6b65\u9aa4\uff1a<\/p>\n<ol>\n<li><strong>\u8bbf\u95ee\u6a21\u578b\u9875\u9762<\/strong><br \/>\n\u6253\u5f00\u00a0<code>https:\/\/huggingface.co\/collections\/google\/medgemma-release-680aade845f90bec6a3f60c4<\/code>\uff0c\u6d4f\u89c8 MedGemma \u6a21\u578b\u96c6\u5408\u3002\u9875\u9762\u5305\u542b 4B \u548c 27B \u53c2\u6570\u6a21\u578b\u7684\u4e0b\u8f7d\u94fe\u63a5\u548c\u6587\u6863\u3002<\/li>\n<li><strong>\u73af\u5883\u51c6\u5907<\/strong>\n<ul>\n<li>\u786e\u4fdd\u5b89\u88c5 Python 3.8 \u6216\u66f4\u9ad8\u7248\u672c\u3002<\/li>\n<li>\u5b89\u88c5 Hugging Face \u7684 Transformers \u5e93\uff0c\u8fd0\u884c\u4ee5\u4e0b\u547d\u4ee4\uff1a\n<pre><code>pip install transformers\r\n<\/code><\/pre>\n<\/li>\n<li>\u5b89\u88c5 PyTorch \u6216 TensorFlow\uff08\u6839\u636e\u6a21\u578b\u9700\u6c42\u9009\u62e9\uff09\u3002\u4f8b\u5982\uff0c\u5b89\u88c5 PyTorch\uff1a\n<pre><code>pip install torch\r\n<\/code><\/pre>\n<\/li>\n<li>\u5982\u679c\u5904\u7406\u56fe\u50cf\u6570\u636e\uff0c\u9700\u5b89\u88c5\u989d\u5916\u5e93\u5982\u00a0<code>Pillow<\/code>\uff1a\n<pre><code>pip install Pillow\r\n<\/code><\/pre>\n<\/li>\n<\/ul>\n<\/li>\n<li><strong>\u4e0b\u8f7d\u6a21\u578b<\/strong><br \/>\n\u5728 Hugging Face \u6a21\u578b\u9875\u9762\uff0c\u9009\u62e9\u9700\u8981\u7684 MedGemma \u53d8\u4f53\uff08\u5982\u00a0<code>google\/medgemma-4b-it<\/code>\u00a0\u6216\u00a0<code>google\/medgemma-27b-multimodal<\/code>\uff09\u3002\u4f7f\u7528\u4ee5\u4e0b\u4ee3\u7801\u4e0b\u8f7d\u5e76\u52a0\u8f7d\u6a21\u578b\uff1a<\/p>\n<pre><code>from transformers import AutoModel, AutoTokenizer\r\nmodel_name = \"google\/medgemma-4b-it\"\r\ntokenizer = AutoTokenizer.from_pretrained(model_name)\r\nmodel = AutoModel.from_pretrained(model_name)\r\n<\/code><\/pre>\n<p>27B \u6a21\u578b\u9700\u8981\u66f4\u591a\u5185\u5b58\uff0c\u5efa\u8bae\u4f7f\u7528\u81f3\u5c11 16GB \u663e\u5b58\u7684 GPU\u3002<\/li>\n<li><strong>\u8fd0\u884c\u73af\u5883<\/strong><br \/>\nMedGemma \u6a21\u578b\u652f\u6301\u5728\u5355 GPU \u4e0a\u8fd0\u884c\uff0c\u9002\u5408\u672c\u5730\u5f00\u53d1\u6216\u4e91\u7aef\u90e8\u7f72\u3002\u63a8\u8350\u4f7f\u7528 Google Cloud \u6216 Hugging Face Inference Endpoints \u8fdb\u884c\u90e8\u7f72\uff0c\u5177\u4f53\u53c2\u8003\u00a0<code>https:\/\/gke-ai-labs.dev\/<\/code> \u7684\u90e8\u7f72\u6307\u5357\u3002<\/li>\n<\/ol>\n<h3>\u4e3b\u8981\u529f\u80fd\u64cd\u4f5c<\/h3>\n<h4>1. \u533b\u7597\u6587\u672c\u5904\u7406<\/h4>\n<p>MedGemma \u53ef\u5904\u7406\u533b\u7597\u6587\u672c\uff0c\u5982\u751f\u6210\u62a5\u544a\u6216\u56de\u7b54\u533b\u7597\u95ee\u9898\u3002\u64cd\u4f5c\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<ul>\n<li><strong>\u8f93\u5165\u51c6\u5907<\/strong>\uff1a\u51c6\u5907\u533b\u7597\u76f8\u5173\u6587\u672c\uff0c\u4f8b\u5982\u4e00\u6bb5\u7535\u5b50\u5065\u5eb7\u8bb0\u5f55\u6216\u533b\u7597\u95ee\u9898\u3002<\/li>\n<li><strong>\u4ee3\u7801\u793a\u4f8b<\/strong>\uff1a\n<pre><code>input_text = \"\u60a3\u8005\u80f8\u90e8 X \u5149\u663e\u793a\u80ba\u90e8\u9634\u5f71\uff0c\u53ef\u80fd\u662f\u4ec0\u4e48\u539f\u56e0\uff1f\"\r\ninputs = tokenizer(input_text, return_tensors=\"pt\")\r\noutputs = model.generate(**inputs, max_length=200)\r\nresponse = tokenizer.decode(outputs[0], skip_special_tokens=True)\r\nprint(response)\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u7ed3\u679c<\/strong>\uff1a\u6a21\u578b\u4f1a\u751f\u6210\u53ef\u80fd\u7684\u8bca\u65ad\u89e3\u91ca\u6216\u5efa\u8bae\uff0c\u57fa\u4e8e\u5176\u5728\u533b\u7597\u6587\u672c\u4e0a\u7684\u8bad\u7ec3\u3002<\/li>\n<\/ul>\n<h4>2. \u533b\u7597\u56fe\u50cf\u7406\u89e3<\/h4>\n<p>MedGemma \u7684\u591a\u6a21\u6001\u6a21\u578b\u652f\u6301\u5206\u6790\u533b\u7597\u5f71\u50cf\uff08\u5982 X \u5149\u3001\u76ae\u80a4\u56fe\u50cf\uff09\u3002\u64cd\u4f5c\u6b65\u9aa4\uff1a<\/p>\n<ul>\n<li><strong>\u56fe\u50cf\u9884\u5904\u7406<\/strong>\uff1a\u5c06\u56fe\u50cf\u8f6c\u6362\u4e3a\u6a21\u578b\u53ef\u63a5\u53d7\u7684\u683c\u5f0f\uff08\u5982 PNG \u6216 JPEG\uff09\u3002<\/li>\n<li><strong>\u4ee3\u7801\u793a\u4f8b<\/strong>\uff08\u4ee5 4B \u591a\u6a21\u6001\u6a21\u578b\u4e3a\u4f8b\uff09\uff1a\n<pre><code>from PIL import Image\r\nimport torch\r\nimage = Image.open(\"chest_xray.png\").convert(\"RGB\")\r\ninputs = tokenizer(text=\"\u63cf\u8ff0\u8fd9\u5f20\u80f8\u90e8 X \u5149\u56fe\u50cf\", images=[image], return_tensors=\"pt\")\r\noutputs = model.generate(**inputs, max_length=200)\r\nresponse = tokenizer.decode(outputs[0], skip_special_tokens=True)\r\nprint(response)\r\n<\/code><\/pre>\n<\/li>\n<li><strong>\u7ed3\u679c<\/strong>\uff1a\u6a21\u578b\u751f\u6210\u56fe\u50cf\u63cf\u8ff0\u6216\u8bca\u65ad\u5efa\u8bae\uff0c\u5982\u201c\u56fe\u50cf\u663e\u793a\u53f3\u80ba\u4e0b\u53f6\u6709\u9634\u5f71\uff0c\u53ef\u80fd\u63d0\u793a\u80ba\u708e\u201d\u3002<\/li>\n<\/ul>\n<h4>3. \u591a\u6a21\u6001\u63a8\u7406<\/h4>\n<p>\u591a\u6a21\u6001\u6a21\u578b\u53ef\u540c\u65f6\u5904\u7406\u6587\u672c\u548c\u56fe\u50cf\u3002\u4f8b\u5982\uff0c\u8f93\u5165\u4e00\u5f20 X \u5149\u56fe\u50cf\u548c\u95ee\u9898\u201c\u6b64\u56fe\u50cf\u662f\u5426\u663e\u793a\u80ba\u708e\u8ff9\u8c61\uff1f\u201d\uff0c\u6a21\u578b\u4f1a\u7ed3\u5408\u56fe\u50cf\u548c\u6587\u672c\u751f\u6210\u56de\u7b54\u3002\u64cd\u4f5c\u4e0e\u4e0a\u8ff0\u7c7b\u4f3c\uff0c\u53ea\u9700\u5728\u00a0<code>tokenizer<\/code>\u00a0\u4e2d\u540c\u65f6\u4f20\u5165\u6587\u672c\u548c\u56fe\u50cf\u3002<\/p>\n<h4>4. \u6a21\u578b\u5fae\u8c03<\/h4>\n<p>\u5f00\u53d1\u8005\u53ef\u6839\u636e\u7279\u5b9a\u4efb\u52a1\u5fae\u8c03\u6a21\u578b\u3002\u6b65\u9aa4\u5982\u4e0b\uff1a<\/p>\n<ul>\n<li>\u6536\u96c6\u7279\u5b9a\u533b\u7597\u6570\u636e\u96c6\uff08\u5982\u81ea\u5b9a\u4e49\u7684\u653e\u5c04\u5b66\u56fe\u50cf\u6216\u6587\u672c\uff09\u3002<\/li>\n<li>\u4f7f\u7528 Hugging Face \u7684\u00a0<code>Trainer<\/code>\u00a0API \u8fdb\u884c\u5fae\u8c03\uff1a\n<pre><code>from transformers import Trainer, TrainingArguments\r\ntraining_args = TrainingArguments(\r\noutput_dir=\".\/medgemma_finetuned\",\r\nper_device_train_batch_size=4,\r\nnum_train_epochs=3,\r\n)\r\ntrainer = Trainer(model=model, args=training_args, train_dataset=your_dataset)\r\ntrainer.train()\r\n<\/code><\/pre>\n<\/li>\n<li>\u4fdd\u5b58\u5fae\u8c03\u540e\u7684\u6a21\u578b\uff0c\u4f9b\u540e\u7eed\u4f7f\u7528\u3002<\/li>\n<\/ul>\n<h3>\u6ce8\u610f\u4e8b\u9879<\/h3>\n<ul>\n<li><strong>\u6570\u636e\u6c61\u67d3\u98ce\u9669<\/strong>\uff1aMedGemma \u5728\u9884\u8bad\u7ec3\u4e2d\u53ef\u80fd\u63a5\u89e6\u8fc7\u516c\u5f00\u533b\u7597\u6570\u636e\uff0c\u5f00\u53d1\u8005\u9700\u4f7f\u7528\u672a\u516c\u5f00\u7684\u6570\u636e\u96c6\u9a8c\u8bc1\u6a21\u578b\u6027\u80fd\uff0c\u4ee5\u786e\u4fdd\u5176\u6cdb\u5316\u80fd\u529b\u3002<\/li>\n<li><strong>\u975e\u4e34\u5e8a\u4f7f\u7528<\/strong>\uff1aMedGemma \u4ec5\u7528\u4e8e\u7814\u7a76\u548c\u5f00\u53d1\uff0c\u672a\u7ecf\u9a8c\u8bc1\u4e0d\u53ef\u7528\u4e8e\u5b9e\u9645\u4e34\u5e8a\u8bca\u65ad\u3002<\/li>\n<li><strong>\u786c\u4ef6\u8981\u6c42<\/strong>\uff1a4B \u6a21\u578b\u9002\u5408\u4f4e\u8d44\u6e90\u73af\u5883\uff0c27B \u6a21\u578b\u9700\u8981\u66f4\u9ad8\u6027\u80fd GPU\u3002<\/li>\n<\/ul>\n<h2>\u5e94\u7528\u573a\u666f<\/h2>\n<ol>\n<li><strong>\u653e\u5c04\u5b66\u62a5\u544a\u751f\u6210<\/strong><br \/>\n\u653e\u5c04\u79d1\u533b\u751f\u53ef\u4f7f\u7528 MedGemma \u5206\u6790 X \u5149\u6216 CT \u56fe\u50cf\uff0c\u751f\u6210\u521d\u6b65\u62a5\u544a\uff0c\u8f85\u52a9\u533b\u751f\u5feb\u901f\u89e3\u8bfb\u5f71\u50cf\u3002<\/li>\n<li><strong>\u533b\u7597\u95ee\u7b54\u7cfb\u7edf<\/strong><br \/>\n\u5f00\u53d1\u8005\u53ef\u6784\u5efa\u533b\u7597\u95ee\u7b54\u673a\u5668\u4eba\uff0c\u5229\u7528 MedGemma \u7684\u6587\u672c\u5904\u7406\u80fd\u529b\u56de\u7b54\u60a3\u8005\u6216\u533b\u5b66\u751f\u7684\u5e38\u89c1\u95ee\u9898\u3002<\/li>\n<li><strong>\u7535\u5b50\u5065\u5eb7\u8bb0\u5f55\u5206\u6790<\/strong><br \/>\n\u533b\u7597\u673a\u6784\u53ef\u4f7f\u7528 27B \u591a\u6a21\u6001\u6a21\u578b\u89e3\u6790\u590d\u6742\u7684 EHR \u6570\u636e\uff0c\u63d0\u53d6\u5173\u952e\u4fe1\u606f\uff0c\u4f18\u5316\u8bca\u7597\u6d41\u7a0b\u3002<\/li>\n<li><strong>\u533b\u5b66\u7814\u7a76\u652f\u6301<\/strong><br \/>\n\u7814\u7a76\u4eba\u5458\u53ef\u5229\u7528 MedGemma \u5206\u6790\u533b\u5b66\u6587\u732e\u6216\u56fe\u50cf\u6570\u636e\u96c6\uff0c\u52a0\u901f\u7814\u7a76\u8fdb\u7a0b\uff0c\u4f8b\u5982\u76ae\u80a4\u75c5\u56fe\u50cf\u5206\u7c7b\u6216\u7ec4\u7ec7\u75c5\u7406\u5206\u6790\u3002<\/li>\n<\/ol>\n<h2>QA<\/h2>\n<ol>\n<li><strong>MedGemma \u53ef\u4ee5\u7528\u4e8e\u5b9e\u9645\u4e34\u5e8a\u8bca\u65ad\u5417\uff1f<\/strong><br \/>\n\u76ee\u524d MedGemma 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