13 citations · 27 across the 21 of their papers we have counts for
4 papers · 1 filter
A Self-Supervised Paradigm for Data-Efficient Medical Foundation Model Pre-training: V-information Optimization Framework
Wenxuan Yang, Hanyu Zhang, Weimin Tan +2
Self-supervised pre-training medical foundation models on large-scale datasets demonstrate exceptional performance. Recent research challenges this common paradigm by introducing d…
Addressing Imbalance for Class Incremental Learning in Medical Image Classification
Xuze Hao, Wenqian Ni, Xuhao Jiang +2
Deep convolutional neural networks have made significant breakthroughs in medical image classification, under the assumption that training samples from all classes are simultaneous…
Context-Aware Iteration Policy Network for Efficient Optical Flow Estimation
Ri Cheng, Ruian He, Xuhao Jiang +3
Existing recurrent optical flow estimation networks are computationally expensive since they use a fixed large number of iterations to update the flow field for each sample. An eff…
A Medical Data-Effective Learning Benchmark for Highly Efficient Pre-training of Foundation Models
Wenxuan Yang, Weimin Tan, Yuqi Sun +1
Foundation models, pre-trained on massive datasets, have achieved unprecedented generalizability. However, is it truly necessary to involve such vast amounts of data in pre-trainin…