AAI_2025_Capstone_Chronicles_Combined

Multi-task vs Single Tasks Combination After performing initial iterations for a simple hybrid classifier training, we recognized the challenge of class imbalance in the provided dataset. Despite generating class weights, the model underperformed for minority classes, such as “Hernia”, potentially for not having enough data representation for that class. We decided to train a model composed of 7 single task models to understand if addressing class imbalance in a more robust way would improve model performance. Each training task corresponded to the data prepared for that task with fully addressed class imbalance. Important distinction is that a negative class did not mean “No Finding”. For a task, a negative class meant an image belonging to any other than the given class. All models shared the same architecture, and were trained separately. Figure A2 demonstrates hybrid classifier architecture. The model consisted of 2 branches, the image branch processed grayscale images through four convolutional blocks, each consisting of Conv2D layers with ReLU activation, each followed by batch normalization, max pooling, and dropout layers. We increased the number of filters progressively (32 → 64 → 128 → 256) to capture both low- and high-level features. A global average pooling layer reduces the spatial dimensions, followed by a fully connected dense layer with 128 units and L2 regularization to extract high-level features. We applied a dropout layer to prevent overfitting. The tabular branch consists of two dense layers with 32 units each and ReLU activation, with batch normalization applied after the first layer to stabilize training. Finally, we added the fusion layer to concatenate outputs of image and tabular branches. The fusion layed is followed by a dense layer with 128 units and dropout for joint feature learning. The final output layer uses a sigmoid activation function to predict probabilities for binary multi-label classification tasks.

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