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Zhiguo Jiang

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8 papers
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8

JBHI Journal 2025 Journal Article

Partial-Label Contrastive Representation Learning for Fine-Grained Biomarkers Prediction From Histopathology Whole Slide Images

  • Yushan Zheng
  • Kun Wu
  • Jun Li
  • Kunming Tang
  • Jun Shi
  • Haibo Wu
  • Zhiguo Jiang
  • Wei Wang

In the domain of histopathology analysis, existing representation learning methods for biomarkers prediction from whole slide images (WSIs) face challenges due to the complexity of tissue subtypes and label noise problems. This paper proposed a novel partial-label contrastive representation learning approach to enhance the discrimination of histopathology image representations for fine-grained biomarkers prediction. We designed a partial-label contrastive clustering (PLCC) module for partial-label disambiguation and a dynamic clustering algorithm to sample the most representative features of each category to the clustering queue during the contrastive learning process. We conducted comprehensive experiments on three gene mutation prediction datasets, including USTC-EGFR, BRCA-HER2, and TCGA-EGFR. The results show that our method outperforms 9 existing methods in terms of Accuracy, AUC, and F1 Score. Specifically, our method achieved an AUC of 0. 950 in EGFR mutation subtyping of TCGA-EGFR and an AUC of 0. 853 in HER2 0/1+/2+/3+ grading of BRCA-HER2, which demonstrates its superiority in fine-grained biomarkers prediction from histopathology whole slide images.

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AAAI Conference 2025 Conference Paper

Promptable Representation Distribution Learning and Data Augmentation for Gigapixel Histopathology WSI Analysis

  • Kunming Tang
  • Zhiguo Jiang
  • Jun Shi
  • Wei Wang
  • Haibo Wu
  • Yushan Zheng

Gigapixel image analysis, particularly for whole slide images (WSIs), often relies on multiple instance learning (MIL). Under the paradigm of MIL, patch image representations are extracted and then fixed during the training of the MIL classifiers for efficiency consideration. However, the invariance of representations makes it difficult to perform data augmentation for WSI-level model training, which significantly limits the performance of the downstream WSI analysis. The current data augmentation methods for gigapixel images either introduce additional computational costs or result in a loss of semantic information, which is hard to meet the requirements for efficiency and stability needed for WSI model training. In this paper, we propose a Promptable Representation Distribution Learning framework (PRDL) for both patch-level representation learning and WSI-level data augmentation. Meanwhile, we explore the use of prompts to guide data augmentation in feature space, which achieves promptable data augmentation for training robust WSI-level models. The experimental results have demonstrated that the proposed method stably outperforms state-of-the-art methods.

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JBHI Journal 2021 Journal Article

Stain Standardization Capsule for Application-Driven Histopathological Image Normalization

  • Yushan Zheng
  • Zhiguo Jiang
  • Haopeng Zhang
  • Fengying Xie
  • Dingyi Hu
  • Shujiao Sun
  • Jun Shi
  • Chenghai Xue

Color consistency is crucial to developing robust deep learning methods for histopathological image analysis. With the increasing application of digital histopathological slides, the deep learning methods are probably developed based on the data from multiple medical centers. This requirement makes it a challenging task to normalize the color variance of histopathological images from different medical centers. In this paper, we propose a novel color standardization module named stain standardization capsule based on the capsule network and the corresponding dynamic routing algorithm. The proposed module can learn and generate uniform stain separation outputs for histopathological images in various color appearance without the reference to manually selected template images. The proposed module is light and can be jointly trained with the application-driven CNN model. The proposed method was validated on three histopathology datasets and a cytology dataset, and was compared with state-of-the-art methods. The experimental results have demonstrated that the SSC module is effective in improving the performance of histopathological image analysis and has achieved the best performance in the compared methods.

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JBHI Journal 2018 Journal Article

Size-Scalable Content-Based Histopathological Image Retrieval From Database That Consists of WSIs

  • Yushan Zheng
  • Zhiguo Jiang
  • Haopeng Zhang
  • Fengying Xie
  • Yibing Ma
  • Huaqiang Shi
  • Yu Zhao

Content-based image retrieval (CBIR) has been widely researched for histopathological images. It is challenging to retrieve contently similar regions from histopathological whole slide images (WSIs) for regions of interest (ROIs) in different size. In this paper, we propose a novel CBIR framework for database that consists of WSIs and size-scalable query ROIs. Each WSI in the database is encoded into a matrix of binary codes. When retrieving, a group of region proposals that have similar size with the query ROI are firstly located in the database through an efficient table-lookup approach. Then, these regions are ranked by a designed multi-binary-code-based similarity measurement. Finally, the top relevant regions and their locations in the WSIs as well as the corresponding diagnostic information are returned to assist pathologists. The effectiveness of the proposed framework is evaluated on a fine-annotated WSI database of epithelial breast tumors. The experimental results have proved that the proposed framework is effective for retrieval from database that consists of WSIs. Specifically, for query ROIs of 4096 $\times$ 4096 pixels, the retrieval precision of the top 20 return has reached 96% and the retrieval time is less than 1. 5 s.

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JBHI Journal 2017 Journal Article

Breast Histopathological Image Retrieval Based on Latent Dirichlet Allocation

  • Yibing Ma
  • Zhiguo Jiang
  • Haopeng Zhang
  • Fengying Xie
  • Yushan Zheng
  • Huaqiang Shi
  • Yu Zhao

In the field of pathology, whole slide image (WSI) has become the major carrier of visual and diagnostic information. Content-based image retrieval among WSIs can aid the diagnosis of an unknown pathological image by finding its similar regions in WSIs with diagnostic information. However, the huge size and complex content of WSI pose several challenges for retrieval. In this paper, we propose an unsupervised, accurate, and fast retrieval method for a breast histopathological image. Specifically, the method presents a local statistical feature of nuclei for morphology and distribution of nuclei, and employs the Gabor feature to describe the texture information. The latent Dirichlet allocation model is utilized for high-level semantic mining. Locality-sensitive hashing is used to speed up the search. Experiments on a WSI database with more than 8000 images from 15 types of breast histopathology demonstrate that our method achieves about 0. 9 retrieval precision as well as promising efficiency. Based on the proposed framework, we are developing a search engine for an online digital slide browsing and retrieval platform, which can be applied in computer-aided diagnosis, pathology education, and WSI archiving and management.

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AAAI Conference 2013 Conference Paper

Joint Object and Pose Recognition Using Homeomorphic Manifold Analysis

  • Haopeng Zhang
  • Tarek El-Gaaly
  • Ahmed Elgammal
  • Zhiguo Jiang

Object recognition is a key precursory challenge in the fields of object manipulation and robotic/AI visual reasoning in general. Recognizing object categories, particular instances of objects and viewpoints/poses of objects are three critical subproblems robots must solve in order to accurately grasp/manipulate objects and reason about their environments. Multi-view images of the same object lie on intrinsic low-dimensional manifolds in descriptor spaces (e. g. visual/depth descriptor spaces). These object manifolds share the same topology despite being geometrically different. Each object manifold can be represented as a deformed version of a unified manifold. The object manifolds can thus be parametrized by its homeomorphic mapping/reconstruction from the unified manifold. In this work, we construct a manifold descriptor from this mapping between homeomorphic manifolds and use it to jointly solve the three challenging recognition sub-problems. We extensively experiment on a challenging multi-modal (i. e. RGBD) dataset and other object pose datasets and achieve state-of-the-art results.

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