555 citations · 1.2k across the 35 of their papers we have counts for
38 papers
Transcriptome-informed multi-modal AI for predicting neoadjuvant therapy response from breast cancer biopsies
Jungkyu Park, Dhruva Biswas, Joseph Cappadona +22
Scarcity of labeled data limits development of deep learning biomarkers in oncology. We develop a two-stage AI model predicting pathological complete response (pCR) to neoadjuvant…
Causal multi-modal AI for personalized chemosensitivity prediction
Dhruva Biswas, Jeroen Berrevoets, Alec McClean +31
Chemotherapy improves survival for some patients with breast cancer, but doctors cannot reliably predict who. Current guidelines rely on recurrence scores as a proxy for treatment…
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…
A Multi-Modal AI System for Screening Mammography: Integrating 2D and 3D Imaging to Improve Breast Cancer Detection in a Prospective Clinical Study
Jungkyu Park, Jan Witowski, Yanqi Xu +8
Although digital breast tomosynthesis (DBT) improves diagnostic performance over full-field digital mammography (FFDM), false-positive recalls remain a concern in breast cancer scr…
Multi-modal AI for comprehensive breast cancer prognostication
Jan Witowski, Ken G. Zeng, Joseph Cappadona +48
Treatment selection in breast cancer is guided by molecular subtypes and clinical characteristics. However, current tools including genomic assays lack the accuracy required for op…
A training regime to learn unified representations from complementary breast imaging modalities
Umang Sharma, Jungkyu Park, Laura Heacock +2
Full Field Digital Mammograms (FFDMs) and Digital Breast Tomosynthesis (DBT) are the two most widely used imaging modalities for breast cancer screening. Although DBT has increased…
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…
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…
Leveraging Neural Networks to Profile Health Care Providers with Application to Medicare Claims
Wenbo Wu, Fan Li, Richard Liu +5
Encompassing numerous nationwide, statewide, and institutional initiatives in the United States, provider profiling has evolved into a major health care undertaking with ubiquitous…
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…
An efficient deep neural network to find small objects in large 3D images
Jungkyu Park, Jakub Chłędowski, Stanisław Jastrzębski +19
3D imaging enables accurate diagnosis by providing spatial information about organ anatomy. However, using 3D images to train AI models is computationally challenging because they…
MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "pai…
Characterizing and overcoming the greedy nature of learning in multi-modal deep neural networks
Nan Wu, Stanisław Jastrzębski, Kyunghyun Cho +1
We hypothesize that due to the greedy nature of learning in multi-modal deep neural networks, these models tend to rely on just one modality while under-fitting the other modalitie…
Generative multitask learning mitigates target-causing confounding
Taro Makino, Krzysztof J. Geras, Kyunghyun Cho
We propose generative multitask learning (GMTL), a simple and scalable approach to causal representation learning for multitask learning. Our approach makes a minor change to the c…
Towards dynamic multi-modal phenotyping using chest radiographs and physiological data
Nasir Hayat, Krzysztof J. Geras, Farah E. Shamout
The healthcare domain is characterized by heterogeneous data modalities, such as imaging and physiological data. In practice, the variety of medical data assists clinicians in deci…
Meta-repository of screening mammography classifiers
Benjamin Stadnick, Jan Witowski, Vishwaesh Rajiv +4
Artificial intelligence (AI) is showing promise in improving clinical diagnosis. In breast cancer screening, recent studies show that AI has the potential to improve early cancer d…
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…
COVID-19 Prognosis via Self-Supervised Representation Learning and Multi-Image Prediction
Anuroop Sriram, Matthew Muckley, Koustuv Sinha +7
The rapid spread of COVID-19 cases in recent months has strained hospital resources, making rapid and accurate triage of patients presenting to emergency departments a necessity. M…
Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization
Stanislaw Jastrzebski, Devansh Arpit, Oliver Astrand +6
The early phase of training a deep neural network has a dramatic effect on the local curvature of the loss function. For instance, using a small learning rate does not guarantee st…
Differences between human and machine perception in medical diagnosis
Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz +18
Deep neural networks (DNNs) show promise in image-based medical diagnosis, but cannot be fully trusted since their performance can be severely degraded by dataset shifts to which h…
Investigating and Simplifying Masking-based Saliency Methods for Model Interpretability
Jason Phang, Jungkyu Park, Krzysztof J. Geras
Saliency maps that identify the most informative regions of an image for a classifier are valuable for model interpretability. A common approach to creating saliency maps involves…
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-…
Understanding the robustness of deep neural network classifiers for breast cancer screening
Witold Oleszkiewicz, Taro Makino, Stanisław Jastrzębski +5
Deep neural networks (DNNs) show promise in breast cancer screening, but their robustness to input perturbations must be better understood before they can be clinically implemented…