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Haibo Wu

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4 papers

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EAAI Journal 2026 Journal Article

Cross-stain knowledge distillation for low-cost lung cancer programmed death ligand-1 assessment with multi-granularity multiple instance learning

Yi Shi
Chong Ge
Fang Zhao
Anli Zhang
Ao Li
Haibo Wu
Minghui Wang

Accurately assessing programmed death ligand-1 (PD-L1) status, recognizing patients potentially responsive to immunotherapies. Since immunohistochemistry (IHC) staining is gold standard in identifying molecular biomarker but often expensive and unattainable, routine hematoxylin and eosin (H&E) staining offers a low-cost alternative. However, H&E images primarily reveal morphological knowledge and inherently lack PD-L1-related molecular information, resulting in a severe mono-stain knowledge limitation. Additionally, most existing approaches analyze gigapixel whole-slide images at only a single magnification, which fails to unravel complex pathological information across granularities, leading to a significant uni-granularity information limitation. Therefore, we propose an innovative cross-stain knowledge distillation with multi-granularity framework, namely CroSMuG. First, to alleviate uni-granularity information limitation, a new multi-granularity multiple instance learning framework is introduced. This is based on macro-micro dual branches, which comprises a macro-branch and a micro-branch to extract global and local pathological information. Furthermore, we develop a novel cross-stain knowledge distillation strategy featuring triple-united distillation loss. Specifically, this approach introduces globality-, locality- and task-aware knowledge distillation, enabling the H&E-based predictive network as a student to learn crucial molecular knowledge from an IHC teacher network. Extensive experiments are conducted on diverse real-world datasets from multiple medical centers, and CroSMuG achieves the superior performance with area under the curve (AUC) of 83. 6 % on internal dataset and 81. 2 % on external dataset. These results highlight the generalizability of CroSMuG for accurate H&E-based PD-L1 assessment, offering the potential for practical applications in lung cancer immunotherapy decision-making in clinical practices.

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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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NeurIPS Conference 2025 Conference Paper

Wavelet Canonical Coherence for Nonstationary Signals

Haibo Wu
Marina Knight
Keiland Cooper
Norbert Fortin
Hernando Ombao

Understanding the evolving dependence between two sets of multivariate signals is fundamental in neuroscience and other domains where sub-networks in a system interact dynamically over time. Despite the growing interest in multivariate time series analysis, existing methods for between-clusters dependence typically rely on the assumption of stationarity and lack the temporal resolution to capture transient, frequency-specific interactions. To overcome this limitation, we propose scale-specific wavelet canonical coherence (WaveCanCoh), a novel framework that extends canonical coherence analysis to the nonstationary setting by leveraging the multivariate locally stationary wavelet model. The proposed WaveCanCoh enables the estimation of time-varying canonical coherence between clusters, providing interpretable insight into scale-specific time-varying interactions between clusters. Through extensive simulation studies, we demonstrate that WaveCanCoh accurately recovers true coherence structures under both locally stationary and general nonstationary conditions. Application to local field potential (LFP) activity data recorded from the hippocampus reveals distinct dynamic coherence patterns between correct and incorrect memory-guided decisions, illustrating capacity of the method to detect behaviorally relevant neural coordination. These results highlight WaveCanCoh as a flexible and principled tool for modeling complex cross-group dependencies in nonstationary multivariate systems. Code for implementing WaveCanCoh is available at https: //github. com/mhaibo/WaveCanCoh. git.

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