Junbo Hu

Hongtao Kang, Die Luo, Li Chen et al.

In practice, digital pathology images are often affected by various factors, resulting in very large differences in color and brightness. Stain normalization can effectively reduce the differences in color and brightness of digital pathology images, thus improving the performance of computer-aided diagnostic systems. Conventional stain normalization methods rely on one or several reference images, but one or several images may not adequately represent the entire dataset. Although learning-based stain normalization methods are a general approach, they use complex deep networks, which not only greatly reduce computational efficiency, but also risk introducing artifacts. Some studies use specialized network structures to enhance computational efficiency and reliability, but these methods are difficult to apply to multi-to-one stain normalization due to insufficient network capacity. In this study, we introduced dynamic-parameter network and proposed a novel method for stain normalization, called ParamNet. ParamNet addresses the challenges of limited network capacity and computational efficiency by introducing dynamic parameters (weights and biases of convolutional layers) into the network design. By effectively leveraging these parameters, ParamNet achieves superior performance in stain normalization while maintaining computational efficiency. Results show ParamNet can normalize one whole slide image (WSI) of 100,000x100,000 within 25s. The code is available at: https://github.com/khtao/ParamNet.

Ziquan Wei, Shenghua Cheng, Junbo Hu et al.

Digital gigapixel whole slide image (WSI) is widely used in clinical diagnosis, and automated WSI analysis is key for computer-aided diagnosis. Currently, analyzing the integrated descriptor of probabilities or feature maps from massive local patches encoded by ResNet classifier is the main manner for WSI-level prediction. Feature representations of the sparse and tiny lesion cells in cervical slides, however, are still challenging, while the unused location representations are available to supply the semantics classification. This study designs a novel and efficient framework with a new module InCNet constructed lightweight model YOLCO (You Only Look Cytology Once). It directly extracts feature inside the single cell (cluster) instead of the traditional way that from image tile with a fixed size. The InCNet (Inline Connection Network) enriches the multi-scale connectivity without efficiency loss. The proposal allows the input size enlarged to megapixel that can stitch the WSI by the average repeats decreased from $10^3\sim10^4$ to $10^1\sim10^2$ for collecting features and predictions at two scales. Based on Transformer for classifying the integrated multi-scale multi-task WSI features, the experimental results appear $0.872$ AUC score better than the best conventional model on our dataset ($n$=2,019) from four scanners. The code is available at https://github.com/Chrisa142857/You-Only-Look-Cytopathology-Once , where the deployment version has the speed $\sim$70 s/WSI.

Yizhi Wang, Li Chen, Qiang Huang et al.

Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and generalizability. General foundation models offer a broader reach but remain limited in capturing subspecialty-specific features and task adaptability. We introduce the Cervical Subspecialty Pathology (CerS-Path) diagnostic system, developed through two synergistic pretraining stages: self-supervised learning on approximately 190 million tissue patches from 140,000 slides to build a cervical-specific feature extractor, and multimodal enhancement with 2.5 million image-text pairs, followed by integration with multiple downstream diagnostic functions. Supporting eight diagnostic functions, including rare cancer classification and multimodal Q&A, CerS-Path surpasses prior foundation models in scope and clinical applicability. Comprehensive evaluations demonstrate a significant advance in cervical pathology, with prospective testing on 3,173 cases across five centers maintaining 99.38% screening sensitivity and excellent generalizability, highlighting its potential for subspecialty diagnostic translation and cervical cancer screening.

Hongtao Kang, Die Luo, Weihua Feng et al.

Stain normalization often refers to transferring the color distribution of the source image to that of the target image and has been widely used in biomedical image analysis. The conventional stain normalization is regarded as constructing a pixel-by-pixel color mapping model, which only depends on one reference image, and can not accurately achieve the style transformation between image datasets. In principle, this style transformation can be well solved by the deep learning-based methods due to its complicated network structure, whereas, its complicated structure results in the low computational efficiency and artifacts in the style transformation, which has restricted the practical application. Here, we use distillation learning to reduce the complexity of deep learning methods and a fast and robust network called StainNet to learn the color mapping between the source image and target image. StainNet can learn the color mapping relationship from a whole dataset and adjust the color value in a pixel-to-pixel manner. The pixel-to-pixel manner restricts the network size and avoids artifacts in the style transformation. The results on the cytopathology and histopathology datasets show that StainNet can achieve comparable performance to the deep learning-based methods. Computation results demonstrate StainNet is more than 40 times faster than StainGAN and can normalize a 100,000x100,000 whole slide image in 40 seconds.