Multi-Task Semi-Supervised Learning for Vascular Network Segmentation and Renal Cell Carcinoma Classification
Résumé
Vascular network analysis is crucial to define the tumoral architecture and then diagnose the cancer subtype. However, automatic vascular network segmentation from Hematoxylin and Eosin (H&E) staining histopathological images is still a challenge due to the background complexity. Moreover, there is a lack of large manually annotated vascular network databases. In this paper, we propose a method that reduces reliance on labeled data through semi-supervised learning (SSL). Additionally, considering the correlation between tumor classification and vascular segmentation, we propose a multi-task learning (MTL) model that can simultaneously segment the vascular network using SSL and predict the tumor class in a supervised context. This multi-task learning procedure offers an end-to-end machine learning solution to joint vascular network segmentation and tumor classification. Experiments were carried out on a database of histopathological images of renal cell carcinoma (RCC) and then tested on both own RCC and open-source TCGA datasets. The results show that the proposed MTL-SSL model outperforms the conventional supervised-learning segmentation approach.
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