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Zhiguo Jiang

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8 papers

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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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YNIMG Journal 2012 Journal Article

A generalized regression model for region of interest analysis of fMRI data

Xiao-Feng Wang
Zhiguo Jiang
Janis J. Daly
Guang H. Yue

In this study functional Magnetic Resonance Imaging (fMRI) was used to evaluate cortical motor network adaptation after a rehabilitation program for upper extremity motor function in chronic stroke patients. Patients and healthy controls were imaged when they attempted to perform shoulder–elbow and wrist–hand movements in a 1. 5T Siemens scanner. We perform fMRI analysis at both single- and group-subject levels. Activated voxel counts are calculated to quantify brain activation in regions of interest. We discuss several candidate regression models for making inference on the count data, and propose an application of a generalized negative-binomial model (GNBM) with structured dispersion in the study. The effects of inappropriate statistical models that ignore the nature of data are addressed through Monte Carlo simulations. Based on the GNBM, significant activation differences are observed in a number of cortical regions for stroke versus control and as a result of treatment; notably, these differences are not detected when the data are analyzed using a conventional linear regression model. Our findings provide an improved functional neuroimaging data analysis protocol, specifically for pixel/voxel counts.

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YNIMG Journal 2012 Journal Article

Strengthened functional connectivity in the brain during muscle fatigue

Zhiguo Jiang
Xiao-Feng Wang
Katarzyna Kisiel-Sajewicz
Jin H. Yan
Guang H. Yue

Fatigue caused by sustaining submaximal-intensity muscle contraction(s) involves increased activation in the brain such as primary motor cortex (M1), primary sensory cortex (S1), premotor and supplementary motor area (PM&SMA) and prefrontal cortex (PFC). The synchronized increases in activation level in these cortical areas suggest fatigue-related strengthening of functional coupling within the motor control network. In the present study, this hypothesis was tested using the cross-correlation based functional connectivity (FC) analysis method. Ten subjects performed a 20-minute intermittent (3. 5s ON/6. 5s OFF, 120 trials total) handgrip task using the right hand at 50% maximal voluntary contraction (MVC) force level while their brain was scanned by a 3T Siemens Trio scanner using echo planar imaging (EPI) sequence. A representative signal time course of the left M1 was extracted by averaging the time course data of a 2-mm cluster of neighboring voxels of local maximal activation foci, which was identified by a general linear model. Two FC activation maps were created for each subject by cross-correlating the time course data of the minimal (the first 10 trials) and significant (the last 10 trials) fatigue stages across all the voxels in the brain to the corresponding representative time course. Histogram and quantile regression analysis were used to compare the FC between the minimal and significant fatigue stages and the results showed a significant increase in FC among multiple cortical regions, including right M1 and bilateral PM&SMA, S1 and PFC. This strengthened FC indicates that when muscle fatigue worsens, many brain regions increase their coupling with the left M1, the primary motor output control center for the right handgrip, to compensate for diminished force generating capability of the muscle in a coordinated fashion by enhancing the descending command for greater muscle recruitment to maintain the same force.

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