5 citations · 13 across the 11 of their papers we have counts for
8 papers · 1 filter
AdvMIM: Adversarial Masked Image Modeling for Semi-Supervised Medical Image Segmentation
Lei Zhu, Jun Zhou, Rick Siow Mong Goh +1
Vision Transformer has recently gained tremendous popularity in medical image segmentation task due to its superior capability in capturing long-range dependencies. However, transf…
Partially Supervised Unpaired Multi-Modal Learning for Label-Efficient Medical Image Segmentation
Lei Zhu, Yanyu Xu, Huazhu Fu +3
Unpaired Multi-Modal Learning (UMML) which leverages unpaired multi-modal data to boost model performance on each individual modality has attracted a lot of research interests in m…
BenchX: A Unified Benchmark Framework for Medical Vision-Language Pretraining on Chest X-Rays
Yang Zhou, Tan Li Hui Faith, Yanyu Xu +4
Medical Vision-Language Pretraining (MedVLP) shows promise in learning generalizable and transferable visual representations from paired and unpaired medical images and reports. Me…
From Generalist to Specialist: Adapting Vision Language Models via Task-Specific Visual Instruction Tuning
Yang Bai, Yang Zhou, Jun Zhou +3
Large vision language models (VLMs) combine large language models with vision encoders, demonstrating promise across various tasks. However, they often underperform in task-specifi…
UrFound: Towards Universal Retinal Foundation Models via Knowledge-Guided Masked Modeling
Kai Yu, Yang Zhou, Yang Bai +5
Retinal foundation models aim to learn generalizable representations from diverse retinal images, facilitating label-efficient model adaptation across various ophthalmic tasks. Des…
Learning Prompt with Distribution-Based Feature Replay for Few-Shot Class-Incremental Learning
Zitong Huang, Ze Chen, Zhixing Chen +6
Few-shot Class-Incremental Learning (FSCIL) aims to continuously learn new classes based on very limited training data without forgetting the old ones encountered. Existing studies…