A novel approach to classification and segmentation of colon cancer imaging towards personalized medicine

Recent advances in the field of pathology coupled with the rapid evolution of machine learning based techniques have revolutionized healthcare practices. Colorectal cancer accounts for one of the top 5 cancers with high incidence (126,240 in 2020) with a high mortality worldwide [1] [2]. Tissue biopsy remains to be the gold standard procedure for accurate diagnosis, treatment planning and prognosis prediction [3]. As an image based modality, pathology has attracted a lot of attention for development of AI algorithms and there has been a steady increase in the number of filings for FDA authorized use of AI algorithms in clinical practice [4]. The SemiCOL Challenge aims to develop computational pathology methods for automatic segmentation and classification of tumor and other tissue classes using H&E stained images. In this paper, we present a novel machine learning framework addressing the SemiCOL Challenge, focusing on semantic segmentation, segmentation-based whole-slide image classification, and effective use of limited annotated data. Our approach leverages deep learning techniques and incorporates data augmentation to improve the accuracy and efficiency of tumor tissue detection and classification in CRC. The proposed method achieves an average Dice score of 0.2785 for segmentation and an AUC score of 0.71 for classification across 20 whole-slide images. This framework has the potential to revolutionize the field of computational pathology, contributing to more efficient and accurate diagnostic tools for colorectal cancer.