Paper: Self-Supervised and Ordinal-Aware Deep Learning Framework for Colorectal Cancer Histopathology Grading.

Contributed to a research paper on self-supervised and ordinal-aware deep learning for automated colorectal cancer detection. The research explored deep learning techniques for medical image analysis, focusing on learning useful representations from limited labeled data and leveraging the ordinal relationship between disease severity levels. The work investigated how these approaches can improve automated classification and support more reliable computer-aided colorectal cancer diagnosis.