Multi-task deep learning framework for enhancing Mayo endoscopic score classification in ulcerative colitis

MTL-based computer-aided diagnosis enables conservative and accurate identification of patients in critical stages, supporting timely and appropriate treatment decisions while reducing the risk of underdiagnosis and delayed care. Furthermore, our results highlight the potential of MTL in overcoming data imbalance issues. Future studies should explore integrating multiple convolutional neural network-based models to further boost classification accuracy.