1.3k citations · 2k across the 59 of their papers we have counts for
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Semi-Supervised Virtual Staining via Morphology Preservation and Histopathological Realism Constraints
Baoshun Wang, Weiping Lin, Linwu Wang +3
Virtual staining aims to computationally generate target-stained histopathological images while reducing the cost and time associated with conventional staining procedures. However…
Evaluating Agentic Harness Systems for Autonomous Computational Pathology
Jie Lin, Zongyi Chen, Qiaoling Zheng +6
Autonomous computational pathology (ACP) converts high-level pathology analysis goals into executable, traceable and clinically bounded workflows. Realizing this capability require…
Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development
Zhongying Deng, Cheng Tang, Ziyan Huang +124
Foundation models have demonstrated remarkable success across diverse domains and tasks, primarily due to the thrive of large-scale, diverse, and high-quality datasets. However, in…
Federated Modality-specific Encoders and Partially Personalized Fusion Decoder for Multimodal Brain Tumor Segmentation
Hong Liu, Dong Wei, Qian Dai +3
Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications.…
Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation
Hong Liu, Dong Wei, Qiong Peng +4
Computed Tomography Report Generation (CTRG) aims to automate the clinical radiology reporting process, thereby reducing the workload of report writing and facilitating patient car…
A multi-center analysis of deep learning methods for video polyp detection and segmentation
Noha Ghatwary, Pedro Chavarias Solano, Mohamed Ramzy Ibrahim +24
Colonic polyps are well-recognized precursors to colorectal cancer (CRC), typically detected during colonoscopy. However, the variability in appearance, location, and size of these…
FDP: A Frequency-Decomposition Preprocessing Pipeline for Unsupervised Anomaly Detection in Brain MRI
Hao Li, Zhenfeng Zhuang, Jingyu Lin +5
Due to the diversity of brain anatomy and the scarcity of annotated data, supervised anomaly detection for brain MRI remains challenging, driving the development of unsupervised an…
Libra-MIL: Multimodal Prototypes Stereoscopic Infused with Task-specific Language Priors for Few-shot Whole Slide Image Classification
Zhenfeng Zhuang, Fangyu Zhou, Liansheng Wang
While Large Language Models (LLMs) are emerging as a promising direction in computational pathology, the substantial computational cost of giga-pixel Whole Slide Images (WSIs) nece…
Instance-Aware Robust Consistency Regularization for Semi-Supervised Nuclei Instance Segmentation
Zenan Lin, Wei Li, Jintao Chen +5
Nuclei instance segmentation in pathological images is crucial for downstream tasks such as tumor microenvironment analysis. However, the high cost and scarcity of annotated data l…
CMIL: Synchronizing Semantic and Topological Causalities in Multiple Instance Learning for Robust and Interpretable Survival Analysis
Min Cen, Zhenfeng Zhuang, Yuzhe Zhang +5
Graph-based Multiple Instance Learning (MIL) is widely used in survival analysis with Hematoxylin and Eosin (H\&E)-stained whole slide images (WSIs) due to its ability to capture t…
Enhancing WSI-Based Survival Analysis with Report-Auxiliary Self-Distillation
Zheng Wang, Hong Liu, Danyi Li +4
Survival analysis based on Whole Slide Images (WSIs) is crucial for evaluating cancer prognosis, as they offer detailed microscopic information essential for predicting patient out…
Efficient Medical Vision-Language Alignment Through Adapting Masked Vision Models
Chenyu Lian, Hong-Yu Zhou, Dongyun Liang +2
Medical vision-language alignment through cross-modal contrastive learning shows promising performance in image-text matching tasks, such as retrieval and zero-shot classification.…
FMaMIL: Frequency-Driven Mamba Multi-Instance Learning for Weakly Supervised Lesion Segmentation in Medical Images
Hangbei Cheng, Xiaorong Dong, Xueyu Liu +6
Accurate lesion segmentation in histopathology images is essential for diagnostic interpretation and quantitative analysis, yet it remains challenging due to the limited availabili…
Federated Self-supervised Domain Generalization for Label-efficient Polyp Segmentation
Xinyi Tan, Jiacheng Wang, Liansheng Wang
Employing self-supervised learning (SSL) methodologies assumes par-amount significance in handling unlabeled polyp datasets when building deep learning-based automatic polyp segmen…
Beyond Diagnostic Performance: Revealing and Quantifying Ethical Risks in Pathology Foundation Models
Weiping Lin, Shen Liu, Runchen Zhu +3
Pathology foundation models (PFMs), as large-scale pre-trained models tailored for computational pathology, have significantly advanced a wide range of applications. Their ability…
Unlocking the Potential of Weakly Labeled Data: A Co-Evolutionary Learning Framework for Abnormality Detection and Report Generation
Jinghan Sun, Dong Wei, Zhe Xu +7
Anatomical abnormality detection and report generation of chest X-ray (CXR) are two essential tasks in clinical practice. The former aims at localizing and characterizing cardiopul…
Dynamic Entity-Masked Graph Diffusion Model for histopathological image Representation Learning
Zhenfeng Zhuang, Min Cen, Yanfeng Li +4
Significant disparities between the features of natural images and those inherent to histopathological images make it challenging to directly apply and transfer pre-trained models…
GAInS: Gradient Anomaly-aware Biomedical Instance Segmentation
Runsheng Liu, Hao Jiang, Yanning Zhou +3
Instance segmentation plays a vital role in the morphological quantification of biomedical entities such as tissues and cells, enabling precise identification and delineation of di…
ViDSOD-100: A New Dataset and a Baseline Model for RGB-D Video Salient Object Detection
Junhao Lin, Lei Zhu, Jiaxing Shen +3
With the rapid development of depth sensor, more and more RGB-D videos could be obtained. Identifying the foreground in RGB-D videos is a fundamental and important task. However, t…
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
Qian Dai, Dong Wei, Hong Liu +3
Most existing federated learning (FL) methods for medical image analysis only considered intramodal heterogeneity, limiting their applicability to multimodal imaging applications.…
HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction
Zhengrui Guo, Jiabo Ma, Yingxue Xu +3
Histopathology serves as the gold standard in cancer diagnosis, with clinical reports being vital in interpreting and understanding this process, guiding cancer treatment and patie…
Generalizable Whole Slide Image Classification with Fine-Grained Visual-Semantic Interaction
Hao Li, Ying Chen, Yifei Chen +5
Whole Slide Image (WSI) classification is often formulated as a Multiple Instance Learning (MIL) problem. Recently, Vision-Language Models (VLMs) have demonstrated remarkable perfo…
Less Could Be Better: Parameter-efficient Fine-tuning Advances Medical Vision Foundation Models
Chenyu Lian, Hong-Yu Zhou, Yizhou Yu +1
Parameter-efficient fine-tuning (PEFT) that was initially developed for exploiting pre-trained large language models has recently emerged as an effective approach to perform transf…
You've Got Two Teachers: Co-evolutionary Image and Report Distillation for Semi-supervised Anatomical Abnormality Detection in Chest X-ray
Jinghan Sun, Dong Wei, Zhe Xu +4
Chest X-ray (CXR) anatomical abnormality detection aims at localizing and characterising cardiopulmonary radiological findings in the radiographs, which can expedite clinical workf…