  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 leverages the advantages of
self-supervised learning in conjunction with employing the available labeled
data. We evaluate our method on the BreakHis dataset, which consists of breast
cancer histopathology images, and demonstrate an increase in classification
accuracy by 1.45% at the image level and 1.42% at the patient level compared to
the state-of-the-art method. This improvement corresponds to 93.63% absolute
accuracy, highlighting our approach's effectiveness in leveraging data
properties to learn more appropriate representation space.
