Uncertainty Aware Sampling Framework of Weak-Label Learning for Histology Image Classification

Asmaa Aljuhani, Ishya Casukhela, Jany Chan, David Liebner, Raghu Machiraju

Abstract: Advances in digital pathology and deep learning have enabled robust disease classification, better diagnosis, and prognosis. In real-world settings, readily available and inexpensive image-level labels from pathology reports are weak, which seriously degrades the performance of deep learning models. Weak image-level labels do not represent the complexity and heterogeneity of the analyzed WSIs. This work presents an importance-based sampling framework for robust histopathology image analysis, Uncertainty-Aware Sampling Framework (UASF). Our experiments demonstrate the effectiveness of UASF when used to grade a highly heterogeneous subtype of soft tissue sarcomas. Furthermore, our proposed model achieves better accuracy when compared to the baseline models by sampling the most relevant tiles.