Gland segmentation is crucial for computer-aided diagnosis of adenocarcinoma. However, Topologically Critical Areas (TCAs), such as background tissues between two adjacent glands, can easily cause under- or over-connection of gland topological structures that may lead to the opposite diagnostic of the malignancy degree. Therefore, we provide a novel perspective for gland segmentation by incorporating gland connectivity information to locate critical errors within TCAs. We propose a Connectivity-Aware Cascade framework (CoCa) that explicitly encodes gland connectivity information into the network to locate all connectivity errors during training and then leverage attention operations to focus on these errors. Since under- or over-connected glands can change the Betti number (e.g., number of connected components) of glands, we design a Connectivity Refinement Module (CRM) to compare the Betti number of each gland to locate connectivity errors. We propose CoCa-Net to mine the topological relations among different biomedical entities to guide gland prediction. We also use contrastive learning to separate pixel embeddings of different classes within TCAs through our connectivity-aware hard example sampling strategy. Extensive experiments on the GlaS and CRAG datasets demonstrate the effectiveness of CoCa over state-of-the-art methods.

Graph-based approaches are successful for histology image classification tasks but still face many challenges, such as: 1) the lack of nuclei-level labels and the significant variations between histology images make it extremely difficult to extract discriminative high-level nuclei features like nuclei type, texture and micro-environment; 2) graph-based approaches cannot handle large-scale cell graph nodes typically contained in histology images; and 3) graph neural networks (GNNs) struggle to learn the long-range dependency of cell graphs. To address the above challenges, we propose a scalable graph-based framework for multi-organ histology image classification. We develop a two-step masked nuclei patches supervised training approach to extract discriminative high-level nuclei features for histology images without nuclei-level labels. Additionally, we introduce a nuclei sampling strategy to make our graph-based framework scalable for large-scale cell graphs. Furthermore, we proposeHierArchicalTransformer Graph NeuralNetwork (HAT-Net+) for cell graph classi- fications. HAT-Net+ adopts Transformer to model the long-range dependency of cell graphs and a parameter-free approach to adaptively fuse different hierarchical graph representations of each layer. We achieved the state-of-the-art results on four public histology image classification datasets: CRC dataset (100%), Extended CRC dataset (98%), UZH dataset (96.9%) and BACH dataset (88%). Unlike other methods, our approach can be used in various histology image classification tasks, even for images without nuclei-level labels, indicating its potential in cancer diagnosis. The code is available athttps://github.com/suyouooooo/HAT-Net.