1 code implementation • 21 Nov 2022 • Siyi Tang, Jared A. Dunnmon, Liangqiong Qu, Khaled K. Saab, Christopher Lee-Messer, Daniel L. Rubin
Multivariate signals are prevalent in various domains, such as healthcare, transportation systems, and space sciences.
no code implementations • 22 Jul 2022 • Nandita Bhaskhar, Daniel L. Rubin, Christopher Lee-Messer
We show that our sequential mistrust scores achieve high drift detection rates: over 90% of the streams show < 20% error for all domains.
no code implementations • 28 Feb 2022 • Alexander Berdichevsky, Mor Peleg, Daniel L. Rubin
Our term normalization algorithm correctly identified 97% of the BI-RADS descriptors in mammography reports.
1 code implementation • 6 Jul 2021 • Miao Zhang, Liangqiong Qu, Praveer Singh, Jayashree Kalpathy-Cramer, Daniel L. Rubin
In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning.
1 code implementation • 17 May 2021 • Vignav Ramesh, Blaine Rister, Daniel L. Rubin
Chest X-rays of coronavirus disease 2019 (COVID-19) patients are frequently obtained to determine the extent of lung disease and are a valuable source of data for creating artificial intelligence models.
1 code implementation • ICLR 2022 • Siyi Tang, Jared A. Dunnmon, Khaled Saab, Xuan Zhang, Qianying Huang, Florian Dubost, Daniel L. Rubin, Christopher Lee-Messer
Automated seizure detection and classification from electroencephalography (EEG) can greatly improve seizure diagnosis and treatment.
no code implementations • 24 Mar 2021 • Sharut Gupta, Praveer Singh, Ken Chang, Liangqiong Qu, Mehak Aggarwal, Nishanth Arun, Ashwin Vaswani, Shruti Raghavan, Vibha Agarwal, Mishka Gidwani, Katharina Hoebel, Jay Patel, Charles Lu, Christopher P. Bridge, Daniel L. Rubin, Jayashree Kalpathy-Cramer
Notably, this approach degrades model performance at the original institution, a phenomenon known as catastrophic forgetting.
1 code implementation • 2 Feb 2021 • Rikiya Yamashita, Jin Long, Snikitha Banda, Jeanne Shen, Daniel L. Rubin
Although various methods such as domain adaptation and domain generalization have evolved to combat this challenge, learning robust and generalizable representations is core to medical image understanding, and continues to be a problem.
no code implementations • 15 Oct 2020 • Siyi Tang, Amirata Ghorbani, Rikiya Yamashita, Sameer Rehman, Jared A. Dunnmon, James Zou, Daniel L. Rubin
In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset.
no code implementations • 13 Jul 2020 • Blaine Rister, Daniel L. Rubin
Neuron death is a complex phenomenon with implications for model trainability: the deeper the network, the lower the probability of finding a valid initialization.
no code implementations • 24 Aug 2019 • Okyaz Eminaga, Mahmoud Abbas, Christian Kunder, Andreas M. Loening, Jeanne Shen, James D. Brooks, Curtis P. Langlotz, Daniel L. Rubin
A well-fitted PlexusNet-based model delivered comparable classification performance (AUC: 0. 963) in distinguishing prostate cancer from healthy tissues, although it was at least 23 times smaller, had a better model calibration and clinical utility than the comparison models.
no code implementations • 29 Apr 2019 • Assaf Hoogi, Brian Wilcox, Yachee Gupta, Daniel L. Rubin
Then, the Self-Attention layer learns to suppress irrelevant regions based on features analysis and highlights salient features useful for a specific task.
no code implementations • 18 Mar 2019 • Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk
For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.
no code implementations • 27 Nov 2018 • Blaine Rister, Darvin Yi, Kaushik Shivakumar, Tomomi Nobashi, Daniel L. Rubin
To achieve the best results from data augmentation, our model uses the intersection-over-union (IOU) loss function, a close relative of the Dice loss.
no code implementations • 15 Jun 2018 • Imon Banerjee, Hailey H. Choi, Terry Desser, Daniel L. Rubin
We propose a scalable computerized approach for large-scale inference of Liver Imaging Reporting and Data System (LI-RADS) final assessment categories in narrative ultrasound (US) reports.
no code implementations • 9 Jan 2018 • Imon Banerjee, Michael Francis Gensheimer, Douglas J. Wood, Solomon Henry, Daniel Chang, Daniel L. Rubin
We propose a deep learning model - Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) for estimating short-term life expectancy (3 months) of the patients by analyzing free-text clinical notes in the electronic medical record, while maintaining the temporal visit sequence.
1 code implementation • 19 Nov 2017 • Imon Banerjee, Sriraman Madhavan, Roger Eric Goldman, Daniel L. Rubin
Radiology reports are a rich resource for advancing deep learning applications in medicine by leveraging the large volume of data continuously being updated, integrated, and shared.
no code implementations • 10 Sep 2017 • Ken Chang, Niranjan Balachandar, Carson K Lam, Darvin Yi, James M. Brown, Andrew Beers, Bruce R. Rosen, Daniel L. Rubin, Jayashree Kalpathy-Cramer
In such cases, sharing a deep learning model is a more attractive alternative.
no code implementations • NeurIPS 2017 • Paroma Varma, Bryan He, Payal Bajaj, Imon Banerjee, Nishith Khandwala, Daniel L. Rubin, Christopher Ré
Obtaining enough labeled data to robustly train complex discriminative models is a major bottleneck in the machine learning pipeline.
no code implementations • 19 Mar 2017 • Assaf Hoogi, John W. Lambert, Yefeng Zheng, Dorin Comaniciu, Daniel L. Rubin
We propose a fully-automated method for accurate and robust detection and segmentation of potentially cancerous lesions found in the liver and in lymph nodes.
no code implementations • 1 Dec 2016 • Imon Banerjee, Lewis Hahn, Geoffrey Sonn, Richard Fan, Daniel L. Rubin
We propose an automated method for detecting aggressive prostate cancer(CaP) (Gleason score >=7) based on a comprehensive analysis of the lesion and the surrounding normal prostate tissue which has been simultaneously captured in T2-weighted MR images, diffusion-weighted images (DWI) and apparent diffusion coefficient maps (ADC).
no code implementations • 17 Jul 2016 • Blaine Rister, Daniel L. Rubin
Finally, that the network as a function of all its parameters is piecewise multi-convex, a generalization of biconvexity.
no code implementations • 12 Jun 2016 • Assaf Hoogi, Christopher F. Beaulieu, Guilherme M. Cunha, Elhamy Heba, Claude B. Sirlin, Sandy Napel, Daniel L. Rubin
We compare our method to a global level set segmentation and to local framework that uses predefined fixed-size square windows.