1 code implementation • NAACL (BioNLP) 2021 • Jean-Benoit Delbrouck, Cassie Zhang, Daniel Rubin
This paper describes the solution of the QIAI lab sent to the Radiology Report Summarization (RRS) challenge at MEDIQA 2021.
no code implementations • 30 Jan 2023 • Rogier van der Sluijs, Nandita Bhaskhar, Daniel Rubin, Curtis Langlotz, Akshay Chaudhari
Thus, it is unknown whether common augmentation strategies employed in Siamese representation learning generalize to medical images and to what extent.
1 code implementation • 18 Oct 2022 • Ali Mirzazadeh, Florian Dubost, Maxwell Pike, Krish Maniar, Max Zuo, Christopher Lee-Messer, Daniel Rubin
We propose an unsupervised fine-tuning method that optimizes the consistency of attention maps and show that it improves both classification performance and the quality of attention maps.
no code implementations • 24 Aug 2022 • Minhaj Nur Alam, Rikiya Yamashita, Vignav Ramesh, Tejas Prabhune, Jennifer I. Lim, R. V. P. Chan, Joelle Hallak, Theodore Leng, Daniel Rubin
CL based pretraining with NST significantly improves DL classification performance, helps the model generalize well (transferable from EyePACS to UIC data), and allows training with small, annotated datasets, therefore reducing ground truth annotation burden of the clinicians.
no code implementations • 17 Jun 2022 • Jupinder Parmar, Khaled Saab, Brian Pogatchnik, Daniel Rubin, Christopher Ré
Domain generalization in medical image classification is an important problem for trustworthy machine learning to be deployed in healthcare.
1 code implementation • 17 May 2022 • Rui Yan, Liangqiong Qu, Qingyue Wei, Shih-Cheng Huang, Liyue Shen, Daniel Rubin, Lei Xing, Yuyin Zhou
The collection and curation of large-scale medical datasets from multiple institutions is essential for training accurate deep learning models, but privacy concerns often hinder data sharing.
no code implementations • 9 May 2022 • Yan-Ran, Wang, Liangqiong Qu, Natasha Diba Sheybani, Xiaolong Luo, Jiangshan Wang, Kristina Elizabeth Hawk, Ashok Joseph Theruvath, Sergios Gatidis, Xuerong Xiao, Allison Pribnow, Daniel Rubin, Heike E. Daldrup-Link
In this study, we utilize the global similarity between baseline and follow-up PET and magnetic resonance (MR) images to develop Masked-LMCTrans, a longitudinal multi-modality co-attentional CNN-Transformer that provides interaction and joint reasoning between serial PET/MRs of the same patient.
1 code implementation • 14 Apr 2022 • Siyi Tang, Amara Tariq, Jared Dunnmon, Umesh Sharma, Praneetha Elugunti, Daniel Rubin, Bhavik N. Patel, Imon Banerjee
Measures to predict 30-day readmission are considered an important quality factor for hospitals as accurate predictions can reduce the overall cost of care by identifying high risk patients before they are discharged.
no code implementations • 28 Nov 2021 • Siddharth Sharma, Florian Dubost, Christopher Lee-Messer, Daniel Rubin
We evaluate an ImageNet pre-trained Mask R-CNN, a standard deep learning model for object detection, on the task of patient detection using our own curated dataset of 45 videos of hospital patients.
no code implementations • 23 Nov 2021 • Yuyin Zhou, Shih-Cheng Huang, Jason Alan Fries, Alaa Youssef, Timothy J. Amrhein, Marcello Chang, Imon Banerjee, Daniel Rubin, Lei Xing, Nigam Shah, Matthew P. Lungren
Despite the routine use of electronic health record (EHR) data by radiologists to contextualize clinical history and inform image interpretation, the majority of deep learning architectures for medical imaging are unimodal, i. e., they only learn features from pixel-level information.
1 code implementation • 18 Nov 2021 • Xiang Bai, Hanchen Wang, Liya Ma, Yongchao Xu, Jiefeng Gan, Ziwei Fan, Fan Yang, Ke Ma, Jiehua Yang, Song Bai, Chang Shu, Xinyu Zou, Renhao Huang, Changzheng Zhang, Xiaowu Liu, Dandan Tu, Chuou Xu, Wenqing Zhang, Xi Wang, Anguo Chen, Yu Zeng, Dehua Yang, Ming-Wei Wang, Nagaraj Holalkere, Neil J. Halin, Ihab R. Kamel, Jia Wu, Xuehua Peng, Xiang Wang, Jianbo Shao, Pattanasak Mongkolwat, Jianjun Zhang, Weiyang Liu, Michael Roberts, Zhongzhao Teng, Lucian Beer, Lorena Escudero Sanchez, Evis Sala, Daniel Rubin, Adrian Weller, Joan Lasenby, Chuangsheng Zheng, Jianming Wang, Zhen Li, Carola-Bibiane Schönlieb, Tian Xia
Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses.
no code implementations • 29 Sep 2021 • Nandita Bhaskhar, Daniel Rubin, Christopher Lee-Messer
We show that TIME-LAPSE is more driven by semantic content compared to other methods, i. e., it is more robust to dataset statistics.
no code implementations • 24 Jun 2021 • Liangqiong Qu, Niranjan Balachandar, Miao Zhang, Daniel Rubin
Specifically, instead of directly training a model for task performance, we develop a novel dual model architecture: a primary model learns the desired task, and an auxiliary "generative replay model" allows aggregating knowledge from the heterogenous clients.
1 code implementation • CVPR 2022 • Liangqiong Qu, Yuyin Zhou, Paul Pu Liang, Yingda Xia, Feifei Wang, Ehsan Adeli, Li Fei-Fei, Daniel Rubin
Federated learning is an emerging research paradigm enabling collaborative training of machine learning models among different organizations while keeping data private at each institution.
no code implementations • 3 Jun 2021 • Florian Dubost, Erin Hong, Max Pike, Siddharth Sharma, Siyi Tang, Nandita Bhaskhar, Christopher Lee-Messer, Daniel Rubin
Optimization plays a key role in the training of deep neural networks.
1 code implementation • 28 Nov 2020 • Florian Dubost, Erin Hong, Nandita Bhaskhar, Siyi Tang, Daniel Rubin, Christopher Lee-Messer
We propose a semi-supervised machine learning training strategy to improve event detection performance on sequential data, such as video recordings, when only sparse labels are available, such as event start times without their corresponding end times.
no code implementations • 3 Sep 2020 • Holger R. Roth, Ken Chang, Praveer Singh, Nir Neumark, Wenqi Li, Vikash Gupta, Sharut Gupta, Liangqiong Qu, Alvin Ihsani, Bernardo C. Bizzo, Yuhong Wen, Varun Buch, Meesam Shah, Felipe Kitamura, Matheus Mendonça, Vitor Lavor, Ahmed Harouni, Colin Compas, Jesse Tetreault, Prerna Dogra, Yan Cheng, Selnur Erdal, Richard White, Behrooz Hashemian, Thomas Schultz, Miao Zhang, Adam McCarthy, B. Min Yun, Elshaimaa Sharaf, Katharina V. Hoebel, Jay B. Patel, Bryan Chen, Sean Ko, Evan Leibovitz, Etta D. Pisano, Laura Coombs, Daguang Xu, Keith J. Dreyer, Ittai Dayan, Ram C. Naidu, Mona Flores, Daniel Rubin, Jayashree Kalpathy-Cramer
Building robust deep learning-based models requires large quantities of diverse training data.
no code implementations • 23 Feb 2020 • Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin
We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.
no code implementations • MIDL 2019 • Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin
Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.
no code implementations • 27 Dec 2019 • Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel Rubin, Greg Zaharchuk
A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.
no code implementations • 18 Dec 2019 • Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel Rubin
We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.
no code implementations • 11 Oct 2019 • Okyaz Eminaga, Yuri Tolkach, Christian Kunder, Mahmood Abbas, Ryan Han, Rosalie Nolley, Axel Semjonow, Martin Boegemann, Sebastian Huss, Andreas Loening, Robert West, Geoffrey Sonn, Richard Fan, Olaf Bettendorf, James Brook, Daniel Rubin
For case usage, these models were applied for the annotation tasks in clinician-oriented pathology reports for prostatectomy specimens.
1 code implementation • 19 Aug 2019 • Ali Hatamizadeh, Assaf Hoogi, Debleena Sengupta, Wuyue Lu, Brian Wilcox, Daniel Rubin, Demetri Terzopoulos
Lesion segmentation is an important problem in computer-assisted diagnosis that remains challenging due to the prevalence of low contrast, irregular boundaries that are unamenable to shape priors.
no code implementations • 26 Mar 2019 • Jared Dunnmon, Alexander Ratner, Nishith Khandwala, Khaled Saab, Matthew Markert, Hersh Sagreiya, Roger Goldman, Christopher Lee-Messer, Matthew Lungren, Daniel Rubin, Christopher Ré
Labeling training datasets has become a key barrier to building medical machine learning models.
no code implementations • ICLR Workshop LLD 2019 • Khaled Saab, Jared Dunnmon, Alexander Ratner, Daniel Rubin, Christopher Re
Supervised machine learning models for high-value computer vision applications such as medical image classification often require large datasets labeled by domain experts, which are slow to collect, expensive to maintain, and static with respect to changes in the data distribution.
no code implementations • 27 Feb 2019 • Imon Banerjee, Luis de Sisternes, Joelle Hallak, Theodore Leng, Aaron Osborne, Mary Durbin, Daniel Rubin
We propose a hybrid sequential deep learning model to predict the risk of AMD progression in non-exudative AMD eyes at multiple timepoints, starting from short-term progression (3-months) up to long-term progression (21-months).
2 code implementations • 13 Feb 2018 • Haque Ishfaq, Assaf Hoogi, Daniel Rubin
Deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity, by relying on the embedding learned from metric learning.
no code implementations • 17 May 2017 • Darvin Yi, Rebecca Lynn Sawyer, David Cohn III, Jared Dunnmon, Carson Lam, Xuerong Xiao, Daniel Rubin
Breast cancer has the highest incidence and second highest mortality rate for women in the US.