LEARNING DEEP PATHOLOGICAL FEATURES FOR WSI-LEVEL CERVICAL CANCER GRADING

Fully automated cervical cancer grading on the level of Whole Slide Images (WSI) is a challenge task. As WSIs are in gigapixel resolution, it is impossible to train a deep classification neural network with the entire WSIs as inputs. To bypass this problem, we propose a two-stage learning framework. In detail, we propose to first learn patch-level deep pathological features for smear patches via a patch-level feature learning module, which is trained via leveraging the cell instance detection task. Then, we propose to learn WSI-level pathological features from patch-level features for cervical cancer grading. We conduct extensive experiments on our private dataset and make comparisons with rule-based cervical cancer grading methods. Experimental results demonstrate that our proposed deep feature-based WSI-level cervical cancer grading method achieves state-of-the-art performance.