70 citations · 132 across the 15 of their papers we have counts for
7 papers · 1 filter
Understanding differences in applying DETR to natural and medical images
Yanqi Xu, Yiqiu Shen, Carlos Fernandez-Granda +2
Transformer-based detectors have shown success in computer vision tasks with natural images. These models, exemplified by the Deformable DETR, are optimized through complex enginee…
Informative Priors Improve the Reliability of Multimodal Clinical Data Classification
L. Julian Lechuga Lopez, Tim G. J. Rudner, Farah E. Shamout
Machine learning-aided clinical decision support has the potential to significantly improve patient care. However, existing efforts in this domain for principled quantification of…
Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning
Kangning Liu, Weicheng Zhu, Yiqiu Shen +4
Learning representations for individual instances when only bag-level labels are available is a fundamental challenge in multiple instance learning (MIL). Recent works have shown p…
Adaptive Early-Learning Correction for Segmentation from Noisy Annotations
Sheng Liu, Kangning Liu, Weicheng Zhu +2
Deep learning in the presence of noisy annotations has been studied extensively in classification, but much less in segmentation tasks. In this work, we study the learning dynamics…
Weakly-supervised High-resolution Segmentation of Mammography Images for Breast Cancer Diagnosis
Kangning Liu, Yiqiu Shen, Nan Wu +3
In the last few years, deep learning classifiers have shown promising results in image-based medical diagnosis. However, interpreting the outputs of these models remains a challeng…
An interpretable classifier for high-resolution breast cancer screening images utilizing weakly supervised localization
Yiqiu Shen, Nan Wu, Jason Phang +8
Medical images differ from natural images in significantly higher resolutions and smaller regions of interest. Because of these differences, neural network architectures that work…
Breast density classification with deep convolutional neural networks
Nan Wu, Krzysztof J. Geras, Yiqiu Shen +6
Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowle…