16 citations · 21 across the 24 of their papers we have counts for
10 papers · 1 filter
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…