Multi-task deep learning with triplet uncertainty for segmentation and microvascular invasion prediction of hepatocellular carcinoma

Multi-task learning has been widely used for jointly tumor segmentation and classification. Uncertainty estimation of the subtask weight coefficient in multi-task learning has been investigated. However, due to the presence of noise in medical image, data uncertainty will affect the performance of multi-task learning. In addition, model uncertainty has not been conducted for multi-task learning. In this work, we propose a triplet-uncertainty in multi-task deep learning network (TU-MTL), simultaneously considering the uncertainty estimation of subtask weight coefficient, data uncertainty estimation and model uncertainty estimation. Experimental results of clinical hepatocellular carcinoma (HCC) demonstrate the effectiveness of the proposed method.