Model Agnostic Saliency for Weakly Supervised Lesion Detection from Breast DCE-MRI
This work addresses the challenge of reliable lesion detection in medical imaging for healthcare applications, representing a novel method rather than an incremental improvement.
The paper tackled the problem of interpreting deep learning model decisions for lesion detection in medical images by proposing a model-agnostic saliency detector that meets specific conditions for accuracy and reliability, achieving state-of-the-art detection accuracy on breast DCE-MRI data.
There is a heated debate on how to interpret the decisions provided by deep learning models (DLM), where the main approaches rely on the visualization of salient regions to interpret the DLM classification process. However, these approaches generally fail to satisfy three conditions for the problem of lesion detection from medical images: 1) for images with lesions, all salient regions should represent lesions, 2) for images containing no lesions, no salient region should be produced,and 3) lesions are generally small with relatively smooth borders. We propose a new model-agnostic paradigm to interpret DLM classification decisions supported by a novel definition of saliency that incorporates the conditions above. Our model-agnostic 1-class saliency detector (MASD) is tested on weakly supervised breast lesion detection from DCE-MRI, achieving state-of-the-art detection accuracy when compared to current visualization methods.