70 citations · 79 across the 5 of their papers we have counts for
5 papers
Evaluating Generative AI as an Educational Tool for Radiology Resident Report Drafting
Antonio Verdone, Aidan Cardall, Fardeen Siddiqui +10
Objective: Radiology residents require timely, personalized feedback to develop accurate image analysis and reporting skills. Increasing clinical workload often limits attendings'…
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…
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…
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…