CELNet: Evidence Localization for Pathology Images using Weakly Supervised Learning
This addresses the need for interpretable and annotation-efficient methods in clinical pathology applications, representing an incremental improvement over existing techniques.
The paper tackles the problem of weak interpretability and heavy annotation requirements in digital pathology by proposing a weakly supervised learning approach that localizes discriminative evidence for diagnostic labels, achieving competitive performance on histopathologic cancer detection tasks.
Despite deep convolutional neural networks boost the performance of image classification and segmentation in digital pathology analysis, they are usually weak in interpretability for clinical applications or require heavy annotations to achieve object localization. To overcome this problem, we propose a weakly supervised learning-based approach that can effectively learn to localize the discriminative evidence for a diagnostic label from weakly labeled training data. Experimental results show that our proposed method can reliably pinpoint the location of cancerous evidence supporting the decision of interest, while still achieving a competitive performance on glimpse-level and slide-level histopathologic cancer detection tasks.