To enhance lesion localization performance alongside classification in the SelectiveKD framework, several strategies can be implemented. First, integrating a segmentation module within the existing architecture could allow the model to not only classify the presence of cancer but also delineate the exact boundaries of lesions within the DBT slices. This could be achieved by employing a fully convolutional network (FCN) or a U-Net architecture that is trained concurrently with the classification task, utilizing both annotated slices and pseudo-labeled slices for training.
Second, leveraging multi-task learning could be beneficial. By formulating the problem as a joint task of classification and localization, the model can share representations between the two tasks, potentially improving performance on both fronts. The loss function could be designed to incorporate both classification loss and localization loss, ensuring that the model learns to focus on relevant features for both tasks.
Additionally, incorporating attention mechanisms could help the model focus on regions of interest within the DBT slices, enhancing its ability to localize lesions. Attention layers can be integrated into the backbone architecture, allowing the model to weigh the importance of different regions in the input data, thus improving localization accuracy.
Finally, further refinement of the pseudo-labeling process specifically for localization tasks could be explored. This could involve generating bounding boxes or segmentation masks as pseudo-labels, rather than just class labels, thereby providing richer supervisory signals for the localization task.