Add MRI images

.gitattributes

@@ -34,3 +34,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
 model_unet_2d_attention_combineloss.keras filter=lfs diff=lfs merge=lfs -text
+*.nii filter=lfs diff=lfs merge=lfs -text

app.py

@@ -6,30 +6,6 @@ from tensorflow.keras.models import load_model
 from tensorflow.keras import backend as K
 import tensorflow as tf
 
-def dice_coefficient(y_true, y_pred, smooth=1.0):
-    labels_num = 4
-    for i in range(labels_num):
-        y_true_f = K.flatten(y_true[:,:,:,i])
-        y_pred_f = K.flatten(y_pred[:,:,:,i])
-        intersection = K.sum(y_true_f * y_pred_f)
-        loss = ((2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth))
-        if i == 0:
-            total_loss = loss
-        else:
-            total_loss = total_loss + loss
-    total_loss = total_loss / labels_num
-    return total_loss
-
-def dice_loss(y_true, y_pred):
-    return 1 - dice_coefficient(y_true, y_pred)
-
-def combined_loss(y_true, y_pred):
-    return 0.5 * dice_loss(y_true, y_pred) + 0.5 * tf.keras.losses.categorical_crossentropy(y_true, y_pred)
-
-def dice_coefficient_necrotic(y_true, y_pred, epsilon=1e-6):
-    intersection = K.sum(K.abs(y_true[:,:,:,1] * y_pred[:,:,:,1]))
-    return (2. * intersection) / (K.sum(K.square(y_true[:,:,:,1])) + K.sum(K.square(y_pred[:,:,:,1])) + epsilon)
-
 def dice_coefficient(y_true, y_pred, smooth=1.0):
     labels_num = 4
     for i in range(labels_num):
@@ -106,13 +82,19 @@ def predict(image_path):
     # 返回预测结果
     return prediction[0, :, :, 1:4]
 
+# 预先上传的 MRI 图像文件列表
+mri_images_path = ["data/BraTS20_Training_001", "data/BraTS20_Training_002", "data/BraTS20_Training_003", "data/BraTS20_Training_004", "data/BraTS20_Training_005", "data/BraTS20_Training_006"]
+mri_images = []
+for i in range(len(mri_images_path)):
+    mri_images.append(mri_images_path[i] + "_flair.nii")
+
 # 创建 Gradio 接口
 iface = gr.Interface(
     fn=predict,
-    inputs=gr.Image(type="filepath"),
+    inputs=gr.Dropdown(choices=mri_images, label="选择MRI图像"),
     outputs=gr.Image(type="numpy"),
     title="MRI Tumor Segmentation",
-    description="上传一张MRI图像，模型将预测肿瘤区域。"
+    description="选择一个MRI图像，模型将预测肿瘤区域。"
 )
 
 # 启动接口

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