Classification of Breast Cancer Histopathology Images using a Modified Supervised Contrastive Learning Method

arXiv:2405.03642v1 Announce Type: cross
Abstract: Deep neural networks have reached remarkable achievements in medical image processing tasks, specifically classifying and detecting various diseases. However, when confronted with limited data, these networks face a critical vulnerability, often succumbing to overfitting by excessively memorizing the limited information available. This work addresses the challenge mentioned above by improving the supervised contrastive learning method to reduce the impact of false positives. Unlike most existing methods that rely predominantly on fully supervised learning, our approach …

abstract arxiv cancer classification cs.cv cs.lg data diseases face however image image processing images information medical networks neural networks overfitting processing tasks type vulnerability