371 citations · 1.3k across the 68 of their papers we have counts for
10 papers · 1 filter
Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences
Yuetan Chu, Gongning Luo, Longxi Zhou +13
Pulmonary artery-vein segmentation is crucial for disease diagnosis and surgical planning and is traditionally achieved by Computed Tomography Pulmonary Angiography (CTPA). However…
Mitigating Test-Time Bias for Fair Image Retrieval
Fanjie Kong, Shuai Yuan, Weituo Hao +1
We address the challenge of generating fair and unbiased image retrieval results given neutral textual queries (with no explicit gender or race connotations), while maintaining the…
Pushing the Efficiency Limit Using Structured Sparse Convolutions
Vinay Kumar Verma, Nikhil Mehta, Shijing Si +2
Weight pruning is among the most popular approaches for compressing deep convolutional neural networks. Recent work suggests that in a randomly initialized deep neural network, the…
Toward Sustainable Continual Learning: Detection and Knowledge Repurposing of Similar Tasks
Sijia Wang, Yoojin Choi, Junya Chen +2
Most existing works on continual learning (CL) focus on overcoming the catastrophic forgetting (CF) problem, with dynamic models and replay methods performing exceptionally well. H…
Efficient Classification of Very Large Images with Tiny Objects
Fanjie Kong, Ricardo Henao
An increasing number of applications in computer vision, specially, in medical imaging and remote sensing, become challenging when the goal is to classify very large images with ti…
Quantum Tensor Network in Machine Learning: An Application to Tiny Object Classification
Fanjie Kong, Xiao-yang Liu, Ricardo Henao
Tiny object classification problem exists in many machine learning applications like medical imaging or remote sensing, where the object of interest usually occupies a small region…
Proactive Pseudo-Intervention: Causally Informed Contrastive Learning For Interpretable Vision Models
Dong Wang, Yuewei Yang, Chenyang Tao +5
Deep neural networks excel at comprehending complex visual signals, delivering on par or even superior performance to that of human experts. However, ad-hoc visual explanations of…
Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer
Zidi Xiu, Junya Chen, Ricardo Henao +3
Dealing with severe class imbalance poses a major challenge for real-world applications, especially when the accurate classification and generalization of minority classes is of pr…
Weakly supervised cross-domain alignment with optimal transport
Siyang Yuan, Ke Bai, Liqun Chen +6
Cross-domain alignment between image objects and text sequences is key to many visual-language tasks, and it poses a fundamental challenge to both computer vision and natural langu…
Thyroid Cancer Malignancy Prediction From Whole Slide Cytopathology Images
David Dov, Shahar Kovalsky, Jonathan Cohen +3
We consider preoperative prediction of thyroid cancer based on ultra-high-resolution whole-slide cytopathology images. Inspired by how human experts perform diagnosis, our approach…