70 citations · 132 across the 15 of their papers we have counts for
15 papers
Pitfalls of Administrative Censoring in Survival Models with Time-Indexed Inputs
Yanqi Xu, Hui Dai, Carlos Fernandez-Granda +2
Survival models can model time-to-event outcomes using partially observed data. They are widely used in clinical prediction, including cancer risk, disease progression, treatment r…
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…
Leveraging Transformers to Improve Breast Cancer Classification and Risk Assessment with Multi-modal and Longitudinal Data
Yiqiu Shen, Jungkyu Park, Frank Yeung +4
Breast cancer screening, primarily conducted through mammography, is often supplemented with ultrasound for women with dense breast tissue. However, existing deep learning models a…
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…
Minimax Supervised Clustering in the Anisotropic Gaussian Mixture Model: A new take on Robust Interpolation
Stanislav Minsker, Mohamed Ndaoud, Yiqiu Shen
We study the supervised clustering problem under the two-component anisotropic Gaussian mixture model in high dimensions and in the non-asymptotic setting. We first derive a lower…
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…
Reducing false-positive biopsies with deep neural networks that utilize local and global information in screening mammograms
Nan Wu, Zhe Huang, Yiqiu Shen +8
Breast cancer is the most common cancer in women, and hundreds of thousands of unnecessary biopsies are done around the world at a tremendous cost. It is crucial to reduce the rate…
An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department
Farah E. Shamout, Yiqiu Shen, Nan Wu +17
During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-…
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…
Screening Mammogram Classification with Prior Exams
Jungkyu Park, Jason Phang, Yiqiu Shen +5
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new ne…
Globally-Aware Multiple Instance Classifier for Breast Cancer Screening
Yiqiu Shen, Nan Wu, Jason Phang +5
Deep learning models designed for visual classification tasks on natural images have become prevalent in medical image analysis. However, medical images differ from typical natural…
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening
Nan Wu, Jason Phang, Jungkyu Park +29
We present a deep convolutional neural network for breast cancer screening exam classification, trained and evaluated on over 200,000 exams (over 1,000,000 images). Our network ach…
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…