no code implementations • 3 Feb 2022 • Timothy L. Kline, Felipe Kitamura, Ian Pan, Amine M. Korchi, Neil Tenenholtz, Linda Moy, Judy Wawira Gichoya, Igor Santos, Steven Blumer, Misha Ysabel Hwang, Kim-Ann Git, Abishek Shroff, Elad Walach, George Shih, Steve Langer
The goal of this series is to provide resources to not only help improve the review process for A. I.-based medical imaging papers, but to facilitate a standard for the information that is presented within all components of the research study.
1 code implementation • 28 Nov 2020 • Taro Makino, Stanislaw Jastrzebski, Witold Oleszkiewicz, Celin Chacko, Robin Ehrenpreis, Naziya Samreen, Chloe Chhor, Eric Kim, Jiyon Lee, Kristine Pysarenko, Beatriu Reig, Hildegard Toth, Divya Awal, Linda Du, Alice Kim, James Park, Daniel K. Sodickson, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
We compare the two with respect to their robustness to Gaussian low-pass filtering, performing a subgroup analysis on microcalcifications and soft tissue lesions.
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.
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.
In this work, we extend the globally-aware multiple instance classifier, a framework we proposed to address these unique properties of medical images.
In breast cancer screening, radiologists make the diagnosis based on images that are taken from two angles.
We trained and evaluated a localization-based deep CNN for breast cancer screening exam classification on over 200, 000 exams (over 1, 000, 000 images).
Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses.
Moreover, both the global structure and local details play important roles in medical image analysis tasks.
2 code implementations • 20 Mar 2019 • Nan Wu, Jason Phang, Jungkyu Park, Yiqiu Shen, Zhe Huang, Masha Zorin, Stanisław Jastrzębski, Thibault Févry, Joe Katsnelson, Eric Kim, Stacey Wolfson, Ujas Parikh, Sushma Gaddam, Leng Leng Young Lin, Kara Ho, Joshua D. Weinstein, Beatriu Reig, Yiming Gao, Hildegard Toth, Kristine Pysarenko, Alana Lewin, Jiyon Lee, Krystal Airola, Eralda Mema, Stephanie Chung, Esther Hwang, Naziya Samreen, S. Gene Kim, Laura Heacock, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
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).
Breast density classification is an essential part of breast cancer screening.
In our work, we propose to use a multi-view deep convolutional neural network that handles a set of high-resolution medical images.