Nuclei segmentation and classification in Hematoxylin and Eosin (H&E) stained histology images play a vital role in cancer diagnosis, treatment planning, and research. However, accurate segmentation can be hindered by factors like irregular cell shapes, unclear boundaries, and class imbalance. To address these challenges, we propose the Adaptive Gated Attention Fusion Network (AGAFNet), which integrates three innovative attention-based blocks into a U-shaped architecture complemented by dedicated decoders for both segmentation and classification tasks. These blocks comprise the Channel-wise and Spatial Attention Integration Block (CSAIB) for enhanced feature representation and selective focus on informative regions; the Adaptive Gated Convolutional Block (AGCB) for robust feature selection throughout the network; and the Fusion Attention Refinement Block (FARB) for effective information fusion. AGAFNet leverages these elements to provide a robust solution for precise nuclei segmentation and classification in H&E stained histology images. We evaluate the performance of AGAFNet on three large-scale multi-tissue datasets: PanNuke, CoNSeP, and Lizard. The experimental results demonstrate our proposed AGAFNet achieves comparable performance to state-of-the-art methods.

Brain tumor segmentation is a key step in brain cancer diagnosis. Segmentation of brain tumor sub-regions, including necrotic, enhancing, and edematous regions, can provide more detailed guidance for clinical diagnosis. Weakly supervised brain tumor segmentation methods have received much attention because they do not require time-consuming pixel-level annotations. However, existing weakly supervised methods focus on the segmentation of the entire tumor region while ignoring the challenging task of multi-label segmentation for the tumor sub-regions. In this article, we propose a weakly supervised approach to solve the multi-label brain tumor segmentation problem. To the best of our knowledge, it's the first end-to-end multi-label weakly supervised segmentation model applied to brain tumor segmentation. With well-designed loss functions and a contrastive learning pre-training process, our proposed Transformer-based segmentation method (WS-MTST) has the ability to perform segmentation of brain tumor sub-regions. We conduct comprehensive experiments and demonstrate that our method reaches the state-of-the-art on the popular brain tumor dataset BraTS (from 2018 to 2020).