16 citations · 21 across the 24 of their papers we have counts for
28 papers
Can Protein-Derived Knowledge Improve Pathology Foundation Models?
Di Zhang, Zhangpeng Gong, Jiashuai Liu +10
Molecularly guided pathology foundation models (PFMs) exploit transcriptomic or proteomic information to enrich whole-slide image (WSI) representations, yet effectively leveraging…
SpaFactor: Lightweight Spatial Context-Aware Gene Program Modeling for Histology-to-Transcriptomics Inference
Shiting Ruan, Xitong Ling, Qiming He +6
Spatial transcriptomics (ST) profiles gene expression within tissue architecture, but its cost and experimental complexity limit routine use. Predicting spatial expression from rou…
DeCo-MIL: Debiased Counterfactual Reasoning for Long-Tailed Whole Slide Image Analysis
Xiaoxiao Li, Xitong Ling, Jiawen Li +6
Multiple instance learning (MIL) is widely used for weakly supervised whole slide image (WSI) analysis. However, under long-tailed distributions, MIL-based WSI analysis faces a nes…
ALICE: Learning a General-Purpose Pathology Foundation Model from Vision, Vision-Language, and Slide-Level Experts
Jiawen Li, Tian Guan, Huijuan Shi +5
Foundation models are reshaping computational pathology, yet their capabilities remain shaped by pretraining objectives, data sources, and spatial scales, fragmenting complementary…
SlideCheck: Guiding Self-Supervised Pretraining of Pathology Foundation Models via Dataset Distributions
Mingyi He, Xinyi Guo, Xitong Ling +7
Pathology foundation models are pretrained on large streams of WSI-derived patches, while supervision during data construction is often slide-level, sparse, or heterogeneous. This…
Is Class Signal Clustered or Routed in Task-Induced Implicit Neural Representation Weight Spaces?
Xinyi Guo, Mingyi He, Haobin Ding +7
Implicit neural representations (INRs) encode images as neural-network weights, making image classification a problem of weight-space classifiability. A natural geometric hypothesi…
Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction
Weiming Chen, Xitong Ling, Zhenyang Cai +5
Cell-level dense prediction is central to computational pathology, but remains challenging due to fine-grained histological structures, strong domain shifts, and costly dense annot…
A Digital Pathology Resource for Liver Cancer Quantification with Datasets, Benchmarks, and Tools
Ying Xiao, Shimiao Tang, Xitong Ling +11
Liver cancer, especially hepatocellular carcinoma (HCC), imposes a substantial global disease burden. Accurate diagnosis and prognostic assessment directly influence treatment sele…
Splats in Splats++: Robust and Generalizable 3D Gaussian Splatting Steganography
Yijia Guo, Wenkai Huang, Tong Hu +9
3D Gaussian Splatting (3DGS) has recently redefined the paradigm of 3D reconstruction, striking an unprecedented balance between visual fidelity and computational efficiency. As it…
To What Extent Do Token-Level Representations from Pathology Foundation Models Improve Dense Prediction?
Weiming Chen, Xitong Ling, Xidong Wang +10
Pathology foundation models (PFMs) have rapidly advanced and are becoming a common backbone for downstream clinical tasks, offering strong transferability across tissues and instit…
HookMIL: Revisiting Context Modeling in Multiple Instance Learning for Computational Pathology
Xitong Ling, Minxi Ouyang, Xiaoxiao Li +7
Multiple Instance Learning (MIL) has enabled weakly supervised analysis of whole-slide images (WSIs) in computational pathology. However, traditional MIL approaches often lose cruc…
StainNet: Scaling Self-Supervised Foundation Models on Immunohistochemistry and Special Stains for Computational Pathology
Jiawen Li, Jiali Hu, Xitong Ling +6
Foundation models trained with self-supervised learning (SSL) on large-scale histological images have significantly accelerated the development of computational pathology. These mo…
HyperST: Hierarchical Hyperbolic Learning for Spatial Transcriptomics Prediction
Chen Zhang, Yilu An, Ying Chen +7
Spatial Transcriptomics (ST) merges the benefits of pathology images and gene expression, linking molecular profiles with tissue structure to analyze spot-level function comprehens…
Deep Pathomic Learning Defines Prognostic Subtypes and Molecular Drivers in Colorectal Cancer
Zisong Wang, Xuanyu Wang, Hang Chen +7
Precise prognostic stratification of colorectal cancer (CRC) remains a major clinical challenge due to its high heterogeneity. The conventional TNM staging system is inadequate for…
From Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
Yizhi Wang, Li Chen, Qiang Huang +24
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise,…
SAC-MIL: Spatial-Aware Correlated Multiple Instance Learning for Histopathology Whole Slide Image Classification
Yu Bai, Zitong Yu, Haowen Tian +11
We propose Spatial-Aware Correlated Multiple Instance Learning (SAC-MIL) for performing WSI classification. SAC-MIL consists of a positional encoding module to encode position info…
Multimodal Prototype Alignment for Semi-supervised Pathology Image Segmentation
Mingxi Fu, Fanglei Fu, Xitong Ling +4
Pathological image segmentation faces numerous challenges, particularly due to ambiguous semantic boundaries and the high cost of pixel-level annotations. Although recent semi-supe…
Deformable Attention Graph Representation Learning for Histopathology Whole Slide Image Analysis
Mingxi Fu, Xitong Ling, Yuxuan Chen +6
Accurate classification of Whole Slide Images (WSIs) and Regions of Interest (ROIs) is a fundamental challenge in computational pathology. While mainstream approaches often adopt M…
DiagR1: A Vision-Language Model Trained via Reinforcement Learning for Digestive Pathology Diagnosis
Minxi Ouyang, Lianghui Zhu, Yaqing Bao +10
Multimodal large models have shown great potential in automating pathology image analysis. However, current multimodal models for gastrointestinal pathology are constrained by both…
Prune&Comp: Free Lunch for Layer-Pruned LLMs via Iterative Pruning with Magnitude Compensation
Xinrui Chen, Hongxing Zhang, Fanyi Zeng +5
Layer pruning has emerged as a promising technique for compressing large language models (LLMs) while achieving acceleration proportional to the pruning ratio. In this work, we ide…
Subspecialty-Specific Foundation Model for Intelligent Gastrointestinal Pathology
Lianghui Zhu, Xitong Ling, Minxi Ouyang +24
Gastrointestinal (GI) diseases represent a clinically significant burden, necessitating precise diagnostic approaches to optimize patient outcomes. Conventional histopathological d…
PathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
Fang Yan, Jianfeng Wu, Jiawen Li +24
The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveragi…
Cross-Modal Prototype Allocation: Unsupervised Slide Representation Learning via Patch-Text Contrast in Computational Pathology
Yuxuan Chen, Jiawen Li, Jiali Hu +4
With the rapid advancement of pathology foundation models (FMs), the representation learning of whole slide images (WSIs) attracts increasing attention. Existing studies develop hi…
Multimodal Distillation-Driven Ensemble Learning for Long-Tailed Histopathology Whole Slide Images Analysis
Xitong Ling, Yifeng Ping, Jiawen Li +8
Multiple Instance Learning (MIL) plays a significant role in computational pathology, enabling weakly supervised analysis of Whole Slide Image (WSI) datasets. The field of WSI anal…