IEEE Transactions on Medical Imaging

HiCervix: An Extensive Hierarchical Dataset and Benchmark for Cervical Cytology Classification

Cervical cytology is a critical screening strategy for early detection of pre-cancerous and cancerous cervical lesions. The challenge lies in accurately classifying various cervical cytology cell types. Existing automated cervical cytology methods are primarily trained on databases covering a narrow range of coarse-grained cell types, which fail to provide a comprehensive and detailed performance analysis that accurately represents real-world cytopathology conditions. To overcome these limitations, we introduce HiCervix, the most extensive, multi-center cervical cytology dataset currently available to the public. HiCervix includes 40,229 cervical cells from 4,496 whole slide images, categorized into 29 annotated classes. These classes are organized within a three-level hierarchical tree to capture fine-grained subtype information. To exploit the semantic correlation inherent in this hierarchical tree, we propose HierSwin, a hierarchical vision transformer-based classification network. HierSwin serves as a benchmark for detailed feature learning in both coarse-level and fine-level cervical cancer classification tasks. In our comprehensive experiments, HierSwin demonstrated remarkable performance, achieving 92.08% accuracy for coarse-level classification and 82.93% accuracy averaged across all three levels. When compared to board-certified cytopathologists, HierSwin achieved high classification performance (0.8293 versus 0.7359 averaged accuracy), highlighting its potential for clinical applications. This newly released HiCervix dataset, along with our benchmark HierSwin method, is poised to make a substantial impact on the advancement of deep learning algorithms for rapid cervical cancer screening and greatly improve cancer prevention and patient outcomes in real-world clinical settings.

A Task Decomposing and Cell Comparing Method for Cervical Lesion Cell Detection

Automatic detection of cervical lesion cells or cell clumps using cervical cytology images is critical to computer-aided diagnosis (CAD) for accurate, objective, and efficient cervical cancer screening. Recently, many methods based on modern object detectors were proposed and showed great potential for automatic cervical lesion detection. Although effective, several issues still hinder further performance improvement of such known methods, such as large appearance variances between single-cell and multi-cell lesion regions, neglecting normal cells, and visual similarity among abnormal cells. To tackle these issues, we propose a new task decomposing and cell comparing network, called TDCC-Net, for cervical lesion cell detection. Specifically, our task decomposing scheme decomposes the original detection task into two subtasks and models them separately, which aims to learn more efficient and useful feature representations for specific cell structures and then improve the detection performance of the original task. Our cell comparing scheme imitates clinical diagnosis of experts and performs cell comparison with a dynamic comparing module (normal-abnormal cells comparing) and an instance contrastive loss (abnormal-abnormal cells comparing). Comprehensive experiments on a large cervical cytology image dataset confirm the superiority of our method over state-of-the-art methods.

HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide Image Classification

Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which presents a significant challenge. While the multi-instance learning (MIL) paradigm alleviates the computational burden of WSIs, existing MIL methods often overlook hierarchical label correlations, treating fine-grained classification as a flat multi-class classification task. To overcome these limitations, we introduce a novel hierarchical multi-instance learning (HMIL) framework. By facilitating on the hierarchical alignment of inherent relationships between different hierarchy of labels at instance and bag level, our approach provides a more structured and informative learning process. Specifically, HMIL incorporates a class-wise attention mechanism that aligns hierarchical information at both the instance and bag levels. Furthermore, we introduce supervised contrastive learning to enhance the discriminative capability for fine-grained classification and a curriculum-based dynamic weighting module to adaptively balance the hierarchical feature during training. Extensive experiments on our large-scale cytology cervical cancer (CCC) dataset and two public histology datasets, BRACS and PANDA, demonstrate the state-of-the-art class-wise and overall performance of our HMIL framework. Our source code is available at https://github.com/ChengJin-git/HMIL.