Whole Slide Image Classification and Segmentation using Deep Learning

Whole slide imaging is now being used across the world in pathology labs for an accurate diagnosis of biopsy specimens. However, due to the large size of these images, an automatic deep learning-based method is highly desirable for diagnosing. Herein, we propose a two-step methodology for the classification and segmentation of whole-slide image (WSI). First, the patches are extracted from the image and fed into deep learning based techniques like U-Net with its corresponding mask for the accurate segmentation. Further, the cancerous patches are trained for the classification task. During inference, the predicted segmented mask are evaluated in the classification model. Our experimental results demonstrated that the proposed methodology can be used for accurate segmentation and classification.