60 citations · 84 across the 21 of their papers we have counts for
15 papers · 1 filter
Path-Decoupled Hyperbolic Flow Matching for Few-Shot Adaptation
Lin Li, Ziqi Jiang, Gefan Ye +5
Recent advances in cross-modal few-shot adaptation treat visual-semantic alignment as a continuous feature transport problem via Flow Matching (FM). However, we argue that Euclidea…
A Deployment-Friendly Foundational Framework for Efficient Computational Pathology
Yu Cai, Cheng Jin, Zhengyu Zhang +25
Pathology foundation models (PFMs) generalize well across computational pathology tasks but remain costly for gigapixel whole-slide image analysis. Here, we present LitePath, a dep…
A Study of Finetuning Video Transformers for Multi-view Geometry Tasks
Huimin Wu, Kwang-Ting Cheng, Stephen Lin +1
This paper presents an investigation of vision transformer learning for multi-view geometry tasks, such as optical flow estimation, by fine-tuning video foundation models. Unlike p…
Token Merging via Spatiotemporal Information Mining for Surgical Video Understanding
Xixi Jiang, Chen Yang, Dong Zhang +3
Vision Transformer models have shown impressive effectiveness in the surgical video understanding tasks through long-range dependency modeling. However, current methods suffer from…
Relation-R1: Progressively Cognitive Chain-of-Thought Guided Reinforcement Learning for Unified Relation Comprehension
Lin Li, Wei Chen, Jiahui Li +2
Recent advances in multi-modal large language models (MLLMs) have significantly improved object-level grounding and region captioning. However, they remain limited in visual relati…
Memory Efficient Transformer Adapter for Dense Predictions
Dong Zhang, Rui Yan, Pingcheng Dong +1
While current Vision Transformer (ViT) adapter methods have shown promising accuracy, their inference speed is implicitly hindered by inefficient memory access operations, e.g., st…
Generalized Task-Driven Medical Image Quality Enhancement with Gradient Promotion
Dong Zhang, Kwang-Ting Cheng
Thanks to the recent achievements in task-driven image quality enhancement (IQE) models like ESTR, the image enhancement model and the visual recognition model can mutually enhance…
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly Detection
Yi Gu, Yi Lin, Kwang-Ting Cheng +1
Medical anomaly detection (AD) is crucial in pathological identification and localization. Current methods typically rely on uncertainty estimation in deep ensembles to detect anom…
Aligning Medical Images with General Knowledge from Large Language Models
Xiao Fang, Yi Lin, Dong Zhang +2
Pre-trained large vision-language models (VLMs) like CLIP have revolutionized visual representation learning using natural language as supervisions, and demonstrated promising gene…
Self-Supervised Learning for Medical Image Data with Anatomy-Oriented Imaging Planes
Tianwei Zhang, Dong Wei, Mengmeng Zhu +2
Self-supervised learning has emerged as a powerful tool for pretraining deep networks on unlabeled data, prior to transfer learning of target tasks with limited annotation. The rel…
Iterative Online Image Synthesis via Diffusion Model for Imbalanced Classification
Shuhan Li, Yi Lin, Hao Chen +1
Accurate and robust classification of diseases is important for proper diagnosis and treatment. However, medical datasets often face challenges related to limited sample sizes and…
Prompt-Guided Foundation Model Tuning for Pathology Image Classification
Yi Lin, Zhengjie Zhu, Kwang-Ting Cheng +1
Foundation models have become pivotal in advancing computational pathology, particularly for whole slide image (WSI) classification. However, prevailing methodologies often rely on…
BoNuS: Boundary Mining for Nuclei Segmentation with Partial Point Labels
Yi Lin, Zeyu Wang, Dong Zhang +2
Nuclei segmentation is a fundamental prerequisite in the digital pathology workflow. The development of automated methods for nuclei segmentation enables quantitative analysis of t…
Towards Customized Knowledge Distillation for Chip-Level Dense Image Predictions
Dong Zhang, Pingcheng Dong, Long Chen +1
It has been revealed that efficient dense image prediction (EDIP) models designed for AI chips, trained using the knowledge distillation (KD) framework, encounter two key challenge…
Dual-distribution discrepancy with self-supervised refinement for anomaly detection in medical images
Yu Cai, Hao Chen, Xin Yang +2
Medical anomaly detection is a crucial yet challenging task aimed at recognizing abnormal images to assist in diagnosis. Due to the high-cost annotations of abnormal images, most m…