This project focuses on advancing machine learning methods to analyze whole slide imaging (WSI) data for gastrointestinal cancer classification. Specifically, self-supervised learning and multiple-instance learning are employed to perform weakly supervised classification.

This project focuses on enhancing an emotion recognition classifier by iteratively leveraging a Generative Adversarial Network to synthesize additional training data. Through this iterative process, the model aims to increase the accuracy of classifying emotions.

This project is dedicated to digit recognition. The goal is to develop a robust model capable of performing digit detection and the accurate classification of digits from 0 to 99 with plans to expand its capabilities in the future.