DS-MTNet: A dual-stream multi-task network for histopathology localization and histologic risk stratification from small biopsy specimens of bladder cancer.

Accurate histologic risk stratification of bladder cancer (BC) from whole-slide images (WSIs) remains challenging due to complex cellular spatial organization and the limited capability of conventional convolution-based methods to capture topological relationships. To address these challenges, this study presents a dual-stream multi-task network, termed DS-MTNet, for knowledge-driven risk stratification and tumor localization in computational pathology. DS-MTNet integrates two complementary feature learning streams: a local stream that captures fine-grained morphological representations from histopathological images, and a generalized stream that models intercellular spatial topology by transforming nuclei into graph-structured representations. The graph stream explicitly encodes cellular interactions using a GraphSAGE-based architecture, enabling effective characterization of the tumor microenvironment. In addition, a multi-task learning framework with a hard weight control mechanism is employed to jointly optimize cancer region detection and risk stratification, thereby reducing task interference and enhancing knowledge sharing across tasks. Extensive experiments conducted on a dataset of 115 BCE WSIs showed that DS-MTNet outperformed the selected comparative methods for histologic risk stratification under the current experimental setting. Ablation studies further confirm the effectiveness of the dual-stream design and the multi-task learning strategy. Moreover, DS-MTNet provides interpretable decision evidence through tumor localization maps, Grad-CAM visualizations, and graph-based explanations, facilitating transparent knowledge extraction from histopathological data. These results indicate that DS-MTNet offers an effective and interpretable knowledge-based framework for histologic risk stratification, highlighting its potential applicability in intelligent decision-support systems for computational pathology.