Duplicate from ClassCat/Medical-Image-Classification-with-MONAI

Co-authored-by: ClassCat AI Research <[email protected]>

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README.md

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+---
+title: Medical Image Classification With MONAI
+emoji: 🔥
+colorFrom: blue
+colorTo: yellow
+sdk: gradio
+sdk_version: 3.16.1
+app_file: app.py
+pinned: false
+duplicated_from: ClassCat/Medical-Image-Classification-with-MONAI
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+
+
+import torch
+from monai.networks.nets import DenseNet121
+
+import gradio as gr
+
+#from PIL import Image
+
+model = DenseNet121(spatial_dims=2, in_channels=1, out_channels=6)
+model.load_state_dict(torch.load('weights/mednist_model.pth', map_location=torch.device('cpu')))
+
+from monai.transforms import (
+    EnsureChannelFirst,
+    Compose,
+    LoadImage,
+    ScaleIntensity,
+)
+
+test_transforms = Compose(
+        [LoadImage(image_only=True), EnsureChannelFirst(), ScaleIntensity()]
+    )
+
+class_names = [
+    'AbdomenCT', 'BreastMRI', 'CXR', 'ChestCT', 'Hand', 'HeadCT'
+]
+
+import os, glob
+
+#examples_dir = './samples'
+#example_files = glob.glob(os.path.join(examples_dir, '*.jpg'))
+
+def classify_image(image_filepath):
+    input = test_transforms(image_filepath)
+
+    model.eval()
+    with torch.no_grad():
+        pred = model(input.unsqueeze(dim=0))
+    
+    prob = torch.nn.functional.softmax(pred[0], dim=0)
+ 
+    confidences = {class_names[i]: float(prob[i]) for i in range(6)} 
+    print(confidences)
+
+    return confidences
+
+
+with gr.Blocks(title="Medical Image Classification with MONAI - ClassCat",
+            css=".gradio-container {background:mintcream;}"
+        ) as demo:
+    gr.HTML("""<div style="font-family:'Times New Roman', 'Serif'; font-size:16pt; font-weight:bold; text-align:center; color:royalblue;">Medical Image Classification with MONAI</div>""")
+
+    with gr.Row(): 
+        input_image = gr.Image(type="filepath", image_mode="L", shape=(64, 64))        
+        output_label=gr.Label(label="Probabilities", num_top_classes=3)
+
+    send_btn = gr.Button("Infer")
+    send_btn.click(fn=classify_image, inputs=input_image, outputs=output_label)
+
+    with gr.Row():
+        gr.Examples(['./samples/mednist_AbdomenCT00.png'], label='Sample images : AbdomenCT', inputs=input_image)
+        gr.Examples(['./samples/mednist_CXR02.png'], label='CXR', inputs=input_image)
+        gr.Examples(['./samples/mednist_ChestCT08.png'], label='ChestCT', inputs=input_image)
+        gr.Examples(['./samples/mednist_Hand01.png'], label='Hand', inputs=input_image)
+        gr.Examples(['./samples/mednist_HeadCT07.png'], label='HeadCT', inputs=input_image)
+
+#demo.queue(concurrency_count=3)
+demo.launch(debug=True)

requirements.txt

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+torch
+monai

weights/mednist_model.pth

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+size 28437517