The insights gained from the SelectiveKD framework for semi-supervised learning in medical imaging can be broadly applied to various other modalities and tasks. For instance, the principles of knowledge distillation and pseudo-labeling can be utilized in other imaging techniques such as MRI, CT scans, or even in non-imaging tasks like electronic health record analysis.
In the context of MRI or CT imaging, where obtaining annotations can also be challenging, the framework can be adapted to leverage unannotated scans by employing similar strategies of knowledge distillation. By training a teacher model on a limited set of annotated scans, the model can then generate pseudo-labels for unannotated scans, facilitating improved training of a student model.
Moreover, the approach can be extended to tasks such as segmentation of organs or lesions in various imaging modalities. The dual-loss strategy employed in SelectiveKD can be adapted to include segmentation losses, allowing for effective training on both classification and segmentation tasks simultaneously.
Beyond imaging, the concepts of semi-supervised learning can be applied to other domains such as natural language processing (NLP) and speech recognition, where labeled data is often scarce. Techniques such as self-training and the use of weak supervision can enhance model performance in these areas, similar to how they improve cancer detection in DBT.
Overall, the methodologies and insights from this work can significantly contribute to advancing the field of semi-supervised learning across diverse applications, ultimately improving the efficiency and effectiveness of machine learning models in various healthcare and non-healthcare domains.