A Novel Weakly Supervised Semantic Segmentation Ensemble Framework for Medical Imaging

The use of deep learning networks for vision based computer aided diagnostics (CAD) offers a tremendous opportunity for medical practitioners. However, state-of-the-art vision-based CAD systems rely on huge pixel-wise annotated datasets. Such datasets are rarely available, thus severely limiting the applicability of vision-based CAD systems. Hence, semantic segmentation with image labels offers a viable alternative. Semantic segmentation with image labels is well studied in a general context but seldom applied in the medical sector. The major challenge in applying semantic segmentation with image labels in the medical sector is that predicting on medical datasets is more complex than in the general context. Thus, directly applying methods for semantic segmentation with image labels like class activation maps (CAMs) on medical data generates insufficient results. However, state-of-the-art approaches rely on CAMs as a foundation. To address this problem, we propose a framework to extract useful information from particular low-quality segmentation masks. We achieve this by using our observations that the low-quality predictions have very low false negative detections, and multiple low-quality predictions show high variance among each other. We evaluated our framework on the popular multi-modal BRATS and prostate DECATHLON segmentation challenge datasets to demonstrate an improved dice score of up to 8% on BRATS and 6% on DECATHLON datasets compared to the previous state-of-the-art.