diff --git a/.gitignore b/.gitignore index f6cce8d..8cd74c5 100644 --- a/.gitignore +++ b/.gitignore @@ -12,6 +12,8 @@ classifier-resnet-weights.hdf5 ResUNet-weights.hdf5 weights_seg.hdf5 weights.hdf5 +*.keras +*.h5 TCGA*/ # Runtime data ā€” may contain real user data, never commit diff --git a/ResUNet-model.json b/ResUNet-model.json index f367ac4..d143d61 100644 --- a/ResUNet-model.json +++ b/ResUNet-model.json @@ -1 +1 @@ -{"module": "keras.src.models.functional", "class_name": "Functional", "config": {"name": "functional_3", "trainable": true, "layers": [{"module": "keras.layers", "class_name": "InputLayer", "config": {"batch_shape": [null, 256, 256, 3], "dtype": "float32", "sparse": false, "ragged": false, "name": "input_layer_3"}, "registered_name": null, "name": "input_layer_3", "inbound_nodes": []}, {"module": "keras.layers", "class_name": "Conv2D", "config": {"name": "conv2d_40", "trainable": true, "dtype": {"module": "keras", "class_name": "DTypePolicy", "config": {"name": 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"registered_name": "Functional", "build_config": {"input_shape": null}, "compile_config": {}} \ No newline at end of file diff --git a/app.py b/app.py index 7acf2c7..cee7d76 100644 --- a/app.py +++ b/app.py @@ -355,9 +355,16 @@ def load_models(): json_savedModel = json_file.read() json_savedModel = json_savedModel.replace('"class_name": "Model"', '"class_name": "Functional"') secondary_segmentation = tf.keras.models.model_from_json(json_savedModel, custom_objects=custom_objects) - # Use same weights if no separate weights file exists - if os.path.exists('weights_seg.hdf5'): + # Load weights + weights_loaded = False + if os.path.exists('ResUNet-weights.keras'): + secondary_segmentation.load_weights('ResUNet-weights.keras') + weights_loaded = True + elif os.path.exists('weights_seg.hdf5'): secondary_segmentation.load_weights('weights_seg.hdf5') + weights_loaded = True + + if weights_loaded: # Compile EXACTLY like notebook secondary_segmentation.compile( optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), diff --git a/dataset_reports/class_distribution.png b/dataset_reports/class_distribution.png index 97fe1f6..08deaff 100644 Binary files a/dataset_reports/class_distribution.png and b/dataset_reports/class_distribution.png differ diff --git a/dataset_reports/class_imbalance_report.json b/dataset_reports/class_imbalance_report.json index 714469a..cf3d072 100644 --- a/dataset_reports/class_imbalance_report.json +++ b/dataset_reports/class_imbalance_report.json @@ -2,25 +2,25 @@ "class_summary": { "0": { "label": "No Tumor", - "count": 2505, - "percentage": 64.6 + "count": 2556, + "percentage": 65.05 }, "1": { "label": "Tumor Present", "count": 1373, - "percentage": 35.4 + "percentage": 34.95 } }, - "imbalance_ratio": 1.824, + "imbalance_ratio": 1.862, "severity": "MODERATE", "recommended_class_weights": { - "0": 0.7741, - "1": 1.4122 + "0": 0.7686, + "1": 1.4308 }, "suggestions": [ - "Pass class_weight={0: 0.7741, 1: 1.4122} to model.fit().", + "Pass class_weight={0: 0.7686, 1: 1.4308} to model.fit().", "Apply random oversampling on the minority class.", "Use stratified train/val/test splits (stratify=df['mask']).", "Evaluate with F1-score / PR-AUC, not just accuracy." ] -} \ No newline at end of file +} \ No newline at end of file diff --git a/dataset_reports/data_mask_clean.csv b/dataset_reports/data_mask_clean.csv index 5f4cb2b..f153372 100644 --- a/dataset_reports/data_mask_clean.csv +++ b/dataset_reports/data_mask_clean.csv @@ -3176,6 +3176,7 @@ TCGA_HT_8111_19980330,TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_34.tif,TCGA_DU TCGA_HT_8111_19980330,TCGA_DU_7018_19911220/TCGA_DU_7018_19911220_34.tif,TCGA_DU_7018_19911220/TCGA_DU_7018_19911220_34_mask.tif,0 TCGA_HT_8111_19980330,TCGA_DU_7019_19940908/TCGA_DU_7019_19940908_34.tif,TCGA_DU_7019_19940908/TCGA_DU_7019_19940908_34_mask.tif,0 TCGA_HT_8114_19981030,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_34.tif,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_34_mask.tif,1 +TCGA_HT_8114_19981030,TCGA_DU_7294_19890104/TCGA_DU_7294_19890104_34.tif,TCGA_DU_7294_19890104/TCGA_DU_7294_19890104_34_mask.tif,0 TCGA_HT_8114_19981030,TCGA_DU_7013_19860523/TCGA_DU_7013_19860523_34.tif,TCGA_DU_7013_19860523/TCGA_DU_7013_19860523_34_mask.tif,0 TCGA_HT_8114_19981030,TCGA_DU_7008_19830723/TCGA_DU_7008_19830723_34.tif,TCGA_DU_7008_19830723/TCGA_DU_7008_19830723_34_mask.tif,1 TCGA_HT_8114_19981030,TCGA_DU_8163_19961119/TCGA_DU_8163_19961119_34.tif,TCGA_DU_8163_19961119/TCGA_DU_8163_19961119_34_mask.tif,0 @@ -3185,11 +3186,13 @@ TCGA_HT_8114_19981030,TCGA_DU_8162_19961029/TCGA_DU_8162_19961029_34.tif,TCGA_DU TCGA_HT_8114_19981030,TCGA_DU_7301_19911112/TCGA_DU_7301_19911112_34.tif,TCGA_DU_7301_19911112/TCGA_DU_7301_19911112_34_mask.tif,0 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TCGA_HT_8114_19981030,TCGA_DU_A5TT_19980318/TCGA_DU_A5TT_19980318_34.tif,TCGA_DU_A5TT_19980318/TCGA_DU_A5TT_19980318_34_mask.tif,0 +TCGA_HT_8114_19981030,TCGA_DU_A5TY_19970709/TCGA_DU_A5TY_19970709_34.tif,TCGA_DU_A5TY_19970709/TCGA_DU_A5TY_19970709_34_mask.tif,0 TCGA_HT_8114_19981030,TCGA_FG_5962_20000626/TCGA_FG_5962_20000626_34.tif,TCGA_FG_5962_20000626/TCGA_FG_5962_20000626_34_mask.tif,1 TCGA_HT_8114_19981030,TCGA_FG_6689_20020326/TCGA_FG_6689_20020326_34.tif,TCGA_FG_6689_20020326/TCGA_FG_6689_20020326_34_mask.tif,1 TCGA_HT_8114_19981030,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_34.tif,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_34_mask.tif,1 @@ -3225,20 +3228,25 @@ TCGA_HT_A61A_20000127,TCGA_DU_6404_19850629/TCGA_DU_6404_19850629_35.tif,TCGA_DU TCGA_HT_A61A_20000127,TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_35.tif,TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_6408_19860521/TCGA_DU_6408_19860521_35.tif,TCGA_DU_6408_19860521/TCGA_DU_6408_19860521_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_35.tif,TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_35_mask.tif,0 +TCGA_HT_A61A_20000127,TCGA_DU_7018_19911220/TCGA_DU_7018_19911220_35.tif,TCGA_DU_7018_19911220/TCGA_DU_7018_19911220_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7019_19940908/TCGA_DU_7019_19940908_35.tif,TCGA_DU_7019_19940908/TCGA_DU_7019_19940908_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_35.tif,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_DU_7013_19860523/TCGA_DU_7013_19860523_35.tif,TCGA_DU_7013_19860523/TCGA_DU_7013_19860523_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7008_19830723/TCGA_DU_7008_19830723_35.tif,TCGA_DU_7008_19830723/TCGA_DU_7008_19830723_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_DU_8163_19961119/TCGA_DU_8163_19961119_35.tif,TCGA_DU_8163_19961119/TCGA_DU_8163_19961119_35_mask.tif,0 +TCGA_HT_A61A_20000127,TCGA_DU_7299_19910417/TCGA_DU_7299_19910417_35.tif,TCGA_DU_7299_19910417/TCGA_DU_7299_19910417_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7309_19960831/TCGA_DU_7309_19960831_35.tif,TCGA_DU_7309_19960831/TCGA_DU_7309_19960831_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_8162_19961029/TCGA_DU_8162_19961029_35.tif,TCGA_DU_8162_19961029/TCGA_DU_8162_19961029_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7301_19911112/TCGA_DU_7301_19911112_35.tif,TCGA_DU_7301_19911112/TCGA_DU_7301_19911112_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7306_19930512/TCGA_DU_7306_19930512_35.tif,TCGA_DU_7306_19930512/TCGA_DU_7306_19930512_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_7302_19911203/TCGA_DU_7302_19911203_35.tif,TCGA_DU_7302_19911203/TCGA_DU_7302_19911203_35_mask.tif,0 +TCGA_HT_A61A_20000127,TCGA_DU_A5TS_19970726/TCGA_DU_A5TS_19970726_35.tif,TCGA_DU_A5TS_19970726/TCGA_DU_A5TS_19970726_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_A5TP_19970614/TCGA_DU_A5TP_19970614_35.tif,TCGA_DU_A5TP_19970614/TCGA_DU_A5TP_19970614_35_mask.tif,0 +TCGA_HT_A61A_20000127,TCGA_DU_8166_19970322/TCGA_DU_8166_19970322_35.tif,TCGA_DU_8166_19970322/TCGA_DU_8166_19970322_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_8165_19970205/TCGA_DU_8165_19970205_35.tif,TCGA_DU_8165_19970205/TCGA_DU_8165_19970205_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_A5TR_19970726/TCGA_DU_A5TR_19970726_35.tif,TCGA_DU_A5TR_19970726/TCGA_DU_A5TR_19970726_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_DU_A5TT_19980318/TCGA_DU_A5TT_19980318_35.tif,TCGA_DU_A5TT_19980318/TCGA_DU_A5TT_19980318_35_mask.tif,0 +TCGA_HT_A61A_20000127,TCGA_DU_A5TY_19970709/TCGA_DU_A5TY_19970709_35.tif,TCGA_DU_A5TY_19970709/TCGA_DU_A5TY_19970709_35_mask.tif,0 TCGA_HT_A61A_20000127,TCGA_FG_5962_20000626/TCGA_FG_5962_20000626_35.tif,TCGA_FG_5962_20000626/TCGA_FG_5962_20000626_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_FG_6689_20020326/TCGA_FG_6689_20020326_35.tif,TCGA_FG_6689_20020326/TCGA_FG_6689_20020326_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_35.tif,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_35_mask.tif,1 @@ -3253,6 +3261,8 @@ TCGA_HT_A61A_20000127,TCGA_HT_7881_19981015/TCGA_HT_7881_19981015_35.tif,TCGA_HT TCGA_HT_A61A_20000127,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_35.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_35.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_35_mask.tif,1 TCGA_HT_A61A_20000127,TCGA_DU_8164_19970111/TCGA_DU_8164_19970111_35.tif,TCGA_DU_8164_19970111/TCGA_DU_8164_19970111_35_mask.tif,0 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TCGA_DU_7304_19930325,TCGA_FG_A60K_20040224/TCGA_FG_A60K_20040224_56.tif,TCGA_FG_A60K_20040224/TCGA_FG_A60K_20040224_56_mask.tif,1 TCGA_DU_7304_19930325,TCGA_HT_7881_19981015/TCGA_HT_7881_19981015_56.tif,TCGA_HT_7881_19981015/TCGA_HT_7881_19981015_56_mask.tif,0 TCGA_HT_7684_19950816,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_56.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_56_mask.tif,1 @@ -3757,6 +3793,7 @@ TCGA_HT_7684_19950816,TCGA_DU_6407_19860514/TCGA_DU_6407_19860514_57.tif,TCGA_DU TCGA_HT_7684_19950816,TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_57.tif,TCGA_DU_6405_19851005/TCGA_DU_6405_19851005_57_mask.tif,0 TCGA_HT_7684_19950816,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_57.tif,TCGA_DU_7014_19860618/TCGA_DU_7014_19860618_57_mask.tif,0 TCGA_HT_7684_19950816,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_57.tif,TCGA_FG_6690_20020226/TCGA_FG_6690_20020226_57_mask.tif,0 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TCGA_DU_5871_19941206,TCGA_FG_A60K_20040224/TCGA_FG_A60K_20040224_60.tif,TCGA_FG_A60K_20040224/TCGA_FG_A60K_20040224_60_mask.tif,0 TCGA_DU_5871_19941206,TCGA_HT_7881_19981015/TCGA_HT_7881_19981015_60.tif,TCGA_HT_7881_19981015/TCGA_HT_7881_19981015_60_mask.tif,0 TCGA_DU_5871_19941206,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_60.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_60_mask.tif,0 @@ -3870,10 +3914,17 @@ TCGA_DU_6401_19831001,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_80.tif,TCGA_HT TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_80.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_80_mask.tif,0 TCGA_DU_6401_19831001,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_81.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_81_mask.tif,0 TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_81.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_81_mask.tif,0 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TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_85.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_85_mask.tif,0 +TCGA_DU_6401_19831001,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_86.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_86_mask.tif,0 TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_86.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_86_mask.tif,0 +TCGA_DU_6401_19831001,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_87.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_87_mask.tif,0 TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_87.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_87_mask.tif,0 +TCGA_DU_6401_19831001,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_88.tif,TCGA_HT_A61A_20000127/TCGA_HT_A61A_20000127_88_mask.tif,0 TCGA_DU_6401_19831001,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_88.tif,TCGA_HT_A61B_19991127/TCGA_HT_A61B_19991127_88_mask.tif,0 diff --git a/train_segmentation.py b/train_segmentation.py new file mode 100644 index 0000000..7b2e337 --- /dev/null +++ b/train_segmentation.py @@ -0,0 +1,293 @@ +import os +import sys +import pandas as pd +import numpy as np +import tensorflow as tf +from sklearn.model_selection import train_test_split +from tensorflow.keras.layers import * +from tensorflow.keras.models import Model +from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau + +sys.stdout.reconfigure(encoding='utf-8') + +# Ensure we import metrics from local utilities.py +sys.path.append('.') +from utilities import ( + DataGenerator, focal_tversky, tversky_loss, tversky, + dice_coefficient, dice_loss, bce_dice_loss, + iou_score, sensitivity, specificity, precision_metric +) + +# ============== 162-LAYER ATTENTION RESUNET ARCHITECTURE ============== + +def squeeze_excite_block(input_tensor, ratio=16): + filters = input_tensor.shape[-1] + se = GlobalAveragePooling2D()(input_tensor) + se = Dense(filters // ratio, activation='relu')(se) + se = Dense(filters, activation='sigmoid')(se) + se = Reshape([1, 1, filters])(se) + return Multiply()([input_tensor, se]) + +def resblock(X, f, use_se=True): + X_copy = X + # Main path + X = Conv2D(f, kernel_size=(1, 1), strides=(1, 1), + kernel_initializer='he_normal', padding='same')(X) + X = BatchNormalization()(X) + X = Activation('relu')(X) + X = Conv2D(f, kernel_size=(3, 3), strides=(1, 1), + padding='same', kernel_initializer='he_normal')(X) + X = BatchNormalization()(X) + # Short path + X_copy = Conv2D(f, kernel_size=(1, 1), strides=(1, 1), + kernel_initializer='he_normal', padding='same')(X_copy) + X_copy = BatchNormalization()(X_copy) + # Add paths + X = Add()([X, X_copy]) + X = Activation('relu')(X) + if use_se: + X = squeeze_excite_block(X) + return X + +def attention_gate(x, g, inter_channels): + theta_x = Conv2D(inter_channels, kernel_size=(1, 1), strides=(1, 1), padding='same')(x) + phi_g = Conv2D(inter_channels, kernel_size=(1, 1), strides=(1, 1), padding='same')(g) + f = Activation('relu')(Add()([theta_x, phi_g])) + psi_f = Conv2D(1, kernel_size=(1, 1), strides=(1, 1), padding='same')(f) + rate = Activation('sigmoid')(psi_f) + return Multiply()([x, rate]) + +def upsample_concat(x, skip, use_attention=True): + x = UpSampling2D((2, 2))(x) + if use_attention: + skip = attention_gate(skip, x, skip.shape[-1] // 2) + merge = Concatenate()([x, skip]) + return merge + +def build_attention_resunet(input_shape=(256, 256, 3)): + X_input = Input(input_shape) + + # ============== ENCODER ============== + conv1_in = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input) + conv1_in = BatchNormalization()(conv1_in) + conv1_in = Conv2D(32, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_in) + conv1_in = BatchNormalization()(conv1_in) + pool_1 = MaxPool2D(pool_size=(2, 2))(conv1_in) + + conv2_in = resblock(pool_1, 64) + pool_2 = MaxPool2D(pool_size=(2, 2))(conv2_in) + + conv3_in = resblock(pool_2, 128) + pool_3 = MaxPool2D(pool_size=(2, 2))(conv3_in) + + conv4_in = resblock(pool_3, 256) + pool_4 = MaxPool2D(pool_size=(2, 2))(conv4_in) + + # ============== BOTTLENECK ============== + conv5_in = resblock(pool_4, 512) + conv5_in = Conv2D(512, 3, padding='same', dilation_rate=2, kernel_initializer='he_normal')(conv5_in) + conv5_in = BatchNormalization()(conv5_in) + conv5_in = Activation('relu')(conv5_in) + + # ============== DECODER ============== + up_1 = upsample_concat(conv5_in, conv4_in, use_attention=True) + up_1 = resblock(up_1, 256) + + up_2 = upsample_concat(up_1, conv3_in, use_attention=True) + up_2 = resblock(up_2, 128) + + up_3 = upsample_concat(up_2, conv2_in, use_attention=True) + up_3 = resblock(up_3, 64) + + up_4 = upsample_concat(up_3, conv1_in, use_attention=True) + up_4 = resblock(up_4, 32) + + # ============== OUTPUT ============== + output = Dropout(0.3)(up_4) + output = Conv2D(1, (1, 1), padding="same", activation="sigmoid")(output) + + return Model(inputs=X_input, outputs=output) + +# ============== 90-LAYER SIMPLE RESUNET ARCHITECTURE ============== + +def build_simple_resunet(input_shape=(256, 256, 3)): + def simple_resblock(X, f): + X_copy = X + X = Conv2D(f, kernel_size=(1,1), strides=(1,1), kernel_initializer='he_normal')(X) + X = BatchNormalization()(X) + X = Activation('relu')(X) + X = Conv2D(f, kernel_size=(3,3), strides=(1,1), padding='same', kernel_initializer='he_normal')(X) + X = BatchNormalization()(X) + X_copy = Conv2D(f, kernel_size=(1,1), strides=(1,1), kernel_initializer='he_normal')(X_copy) + X_copy = BatchNormalization()(X_copy) + X = Add()([X, X_copy]) + X = Activation('relu')(X) + return X + + def simple_upsample_concat(x, skip): + x = UpSampling2D((2,2))(x) + merge = Concatenate()([x, skip]) + return merge + + X_input = Input(input_shape) + + # Encoder + conv1_in = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(X_input) + conv1_in = BatchNormalization()(conv1_in) + conv1_in = Conv2D(16, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1_in) + conv1_in = BatchNormalization()(conv1_in) + pool_1 = MaxPool2D(pool_size=(2,2))(conv1_in) + + conv2_in = simple_resblock(pool_1, 32) + pool_2 = MaxPool2D(pool_size=(2,2))(conv2_in) + + conv3_in = simple_resblock(pool_2, 64) + pool_3 = MaxPool2D(pool_size=(2,2))(conv3_in) + + conv4_in = simple_resblock(pool_3, 128) + pool_4 = MaxPool2D(pool_size=(2,2))(conv4_in) + + conv5_in = simple_resblock(pool_4, 256) + + # Decoder + up_1 = simple_upsample_concat(conv5_in, conv4_in) + up_1 = simple_resblock(up_1, 128) + + up_2 = simple_upsample_concat(up_1, conv3_in) + up_2 = simple_resblock(up_2, 64) + + up_3 = simple_upsample_concat(up_2, conv2_in) + up_3 = simple_resblock(up_3, 32) + + up_4 = simple_upsample_concat(up_3, conv1_in) + up_4 = simple_resblock(up_4, 16) + + output = Conv2D(1, (1,1), padding="same", activation="sigmoid")(up_4) + + return Model(inputs=X_input, outputs=output) + +# ============== DATA PREPARATION & TRAINING ============== + +def main(): + print("šŸ“‚ Loading data...") + csv_path = 'data_mask.csv' + if not os.path.exists(csv_path): + print(f"āŒ Error: {csv_path} not found.") + sys.exit(1) + + df = pd.read_csv(csv_path) + + # Keep only positive scans (mask == 1) for segmentation task + df_mask = df[df['mask'] == 1].copy() + print(f"šŸ“Š Total samples with tumor: {len(df_mask)}") + + # Train/Val/Test Split (85% train/val, 15% test) + X_train, X_val = train_test_split(df_mask, test_size=0.15, random_state=42) + X_test, X_val = train_test_split(X_val, test_size=0.5, random_state=42) + + print(f" - Train samples: {len(X_train)}") + print(f" - Val samples: {len(X_val)}") + print(f" - Test samples: {len(X_test)}") + + # Create generators + train_gen = DataGenerator( + ids=list(X_train.image_path), + mask=list(X_train.mask_path), + batch_size=16, + shuffle=True + ) + + val_gen = DataGenerator( + ids=list(X_val.image_path), + mask=list(X_val.mask_path), + batch_size=16, + shuffle=False + ) + + test_gen = DataGenerator( + ids=list(X_test.image_path), + mask=list(X_test.mask_path), + batch_size=16, + shuffle=False + ) + + # ---------------- 1. TRAIN ATTENTION RESUNET ---------------- + print("\nšŸ—ļø Building Attention ResUNet (162 layers)...") + model_att = build_attention_resunet() + + # Save model structure as JSON + print("šŸ’¾ Saving Attention ResUNet JSON configuration...") + with open('ResUNet-model.json', 'w') as json_file: + json_file.write(model_att.to_json()) + + model_att.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), + loss=focal_tversky, + metrics=[tversky, dice_coefficient, iou_score, sensitivity, specificity, precision_metric] + ) + + print("šŸš€ Training Attention ResUNet...") + callbacks_att = [ + EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5, restore_best_weights=True), + ModelCheckpoint(filepath="ResUNet-weights.keras", monitor='val_dice_coefficient', mode='max', verbose=1, save_best_only=True), + ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-7, verbose=1) + ] + + history_att = model_att.fit( + train_gen, + epochs=5, + validation_data=val_gen, + callbacks=callbacks_att, + verbose=1 + ) + + # ---------------- 2. TRAIN SIMPLE RESUNET ---------------- + print("\nšŸ—ļø Building Simple ResUNet (90 layers)...") + model_simple = build_simple_resunet() + + # Save model structure as JSON + print("šŸ’¾ Saving Simple ResUNet JSON configuration...") + with open('ResUNet-MRI.json', 'w') as json_file: + json_file.write(model_simple.to_json()) + + model_simple.compile( + optimizer=tf.keras.optimizers.Adam(learning_rate=0.001), + loss=focal_tversky, + metrics=[tversky, dice_coefficient, iou_score, sensitivity, specificity, precision_metric] + ) + + print("šŸš€ Training Simple ResUNet...") + callbacks_simple = [ + EarlyStopping(monitor='val_loss', mode='min', verbose=1, patience=5, restore_best_weights=True), + ModelCheckpoint(filepath="weights_seg.hdf5", monitor='val_dice_coefficient', mode='max', verbose=1, save_best_only=True), + ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=3, min_lr=1e-7, verbose=1) + ] + + history_simple = model_simple.fit( + train_gen, + epochs=5, + validation_data=val_gen, + callbacks=callbacks_simple, + verbose=1 + ) + + # ---------------- EVALUATION ---------------- + print("\nšŸ“Š Evaluating models on Test Set...") + + eval_att = model_att.evaluate(test_gen, verbose=1) + print("\n========================================") + print("Attention ResUNet Test Metrics:") + for name, val in zip(model_att.metrics_names, eval_att): + print(f" - {name}: {val:.4f}") + + eval_simple = model_simple.evaluate(test_gen, verbose=1) + print("\n========================================") + print("Simple ResUNet Test Metrics:") + for name, val in zip(model_simple.metrics_names, eval_simple): + print(f" - {name}: {val:.4f}") + print("========================================\n") + + print("āœ“ Model retraining and evaluation complete!") + +if __name__ == '__main__': + main() diff --git a/utilities.py b/utilities.py index 04a30de..3ecf4be 100644 --- a/utilities.py +++ b/utilities.py @@ -246,7 +246,10 @@ def prediction(test, model, model_seg): year={2018} } ''' + def tversky(y_true, y_pred, smooth=1e-6): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) y_true_pos = K.flatten(y_true) y_pred_pos = K.flatten(y_pred) true_pos = K.sum(y_true_pos * y_pred_pos) @@ -264,6 +267,8 @@ def focal_tversky(y_true, y_pred): return K.pow((1 - pt_1), gamma) def dice_coefficient(y_true, y_pred, smooth=1e-6): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) y_true_f = K.flatten(y_true) y_pred_f = K.flatten(y_pred) intersection = K.sum(y_true_f * y_pred_f) @@ -276,21 +281,29 @@ def bce_dice_loss(y_true, y_pred): return tf.keras.losses.binary_crossentropy(y_true, y_pred) + dice_loss(y_true, y_pred) def iou_score(y_true, y_pred, smooth=1e-6): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) intersection = K.sum(K.abs(y_true * y_pred), axis=[1, 2, 3]) union = K.sum(y_true, [1, 2, 3]) + K.sum(y_pred, [1, 2, 3]) - intersection return K.mean((intersection + smooth) / (union + smooth), axis=0) def sensitivity(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) possible_positives = K.sum(K.round(K.clip(y_true, 0, 1))) return true_positives / (possible_positives + K.epsilon()) def specificity(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) true_negatives = K.sum(K.round(K.clip((1 - y_true) * (1 - y_pred), 0, 1))) possible_negatives = K.sum(K.round(K.clip(1 - y_true, 0, 1))) return true_negatives / (possible_negatives + K.epsilon()) def precision_metric(y_true, y_pred): + y_true = tf.cast(y_true, tf.float32) + y_pred = tf.cast(y_pred, tf.float32) true_positives = K.sum(K.round(K.clip(y_true * y_pred, 0, 1))) predicted_positives = K.sum(K.round(K.clip(y_pred, 0, 1))) return true_positives / (predicted_positives + K.epsilon())