2 citations · 2 across the 9 of their papers we have counts for
9 papers
CACSurv: Concordance-Aligned Comparative Learning with Large Language Models for Cancer Survival Prediction
Tianqi Xiang, Qixiang Zhang, Xinpeng Ding +2
Cancer survival prediction supports treatment planning, risk stratification, and follow-up management. Existing methods use structured clinical variables, whole-slide images, genom…
KHiM-Mamba: Injecting Pathology Knowledge into Mamba via Hidden-State Modulation for Whole Slide Image Analysis
Qixiang Zhang, Yi Li, Tianqi Xiang +4
Whole slide image analysis is commonly formulated as multiple instance learning (MIL), where instance features are contextually updated and aggregated into a slide representation,…
DenseMLLM: Standard Multimodal LLMs for Dense Prediction
Yi Li, Hongze Shen, Lexiang Tang +6
Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in high-level visual understanding. However, extending these models to fine-grained dense predic…
MOC: Meta-Optimized Classifier for Few-Shot Whole Slide Image Classification
Tianqi Xiang, Yi Li, Qixiang Zhang +1
Recent advances in histopathology vision-language foundation models (VLFMs) have shown promise in addressing data scarcity for whole slide image (WSI) classification via zero-shot…
Token Activation Map to Visually Explain Multimodal LLMs
Yi Li, Hualiang Wang, Xinpeng Ding +2
Multimodal large language models (MLLMs) are broadly empowering various fields. Despite their advancements, the explainability of MLLMs remains less explored, hindering deeper unde…
UniEval: Unified Holistic Evaluation for Unified Multimodal Understanding and Generation
Yi Li, Haonan Wang, Qixiang Zhang +4
The emergence of unified multimodal understanding and generation models is rapidly attracting attention because of their ability to enhance instruction-following capabilities while…
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
Zheang Huai, Hui Tang, Yi Li +2
Source-free domain adaptation (SFDA) for segmentation aims at adapting a model trained in the source domain to perform well in the target domain with only the source model and unla…
AllSpark: Reborn Labeled Features from Unlabeled in Transformer for Semi-Supervised Semantic Segmentation
Haonan Wang, Qixiang Zhang, Yi Li +1
Semi-supervised semantic segmentation (SSSS) has been proposed to alleviate the burden of time-consuming pixel-level manual labeling, which leverages limited labeled data along wit…
Exploiting Hierarchical Interactions for Protein Surface Learning
Yiqun Lin, Liang Pan, Yi Li +2
Predicting interactions between proteins is one of the most important yet challenging problems in structural bioinformatics. Intrinsically, potential function sites in protein surf…