no code implementations • 21 Nov 2023 • Carolina A. M. Heming, Mohamed Abdalla, Monish Ahluwalia, Linglin Zhang, Hari Trivedi, Minjae Woo, Benjamin Fine, Judy Wawira Gichoya, Leo Anthony Celi, Laleh Seyyed-Kalantari
Clinical AI model reporting cards should be expanded to incorporate a broad bias reporting of both social and non-social factors.
no code implementations • 2 Nov 2023 • Thiago Santos, Harish Kamath, Christopher R. McAdams, Mary S. Newell, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Constance Lehman, Judy Gichoya, Imon Banerjee, Hari Trivedi
Automated classification of cancer pathology reports can extract information from unstructured reports and categorize each report into structured diagnosis and severity categories.
no code implementations • 8 May 2023 • Linglin Zhang, Beatrice Brown-Mulry, Vineela Nalla, InChan Hwang, Judy Wawira Gichoya, Aimilia Gastounioti, Imon Banerjee, Laleh Seyyed-Kalantari, Minjae Woo, Hari Trivedi
However, after controlling for confounding, we found lower FN risk associates with Other race(RR=0. 828;p=. 050), biopsy-proven benign lesions(RR=0. 927;p=. 011), and mass(RR=0. 921;p=. 010) or asymmetry(RR=0. 854;p=. 040); higher FN risk associates with architectural distortion (RR=1. 037;p<. 001).
no code implementations • 13 Nov 2022 • Ryan Wang, Li-Ching Chen, Lama Moukheiber, Mira Moukheiber, Dana Moukheiber, Zach Zaiman, Sulaiman Moukheiber, Tess Litchman, Kenneth Seastedt, Hari Trivedi, Rebecca Steinberg, Po-Chih Kuo, Judy Gichoya, Leo Anthony Celi
We further propose two fusion schemes, (1) model-level fusion, including bagging and stacking methods using MIMIC-CXR, and (2) data-level fusion, including multi-site data using MIMIC-CXR and Emory-CXR, and multi-modal using MIMIC-CXRs and MIMIC-IV EHR, to improve the overall model performance.
no code implementations • 30 Jun 2022 • Shuja Khalid, Francisco Matos, Ayman Abunimer, Joel Bartlett, Richard Duszak, Michal Horny, Judy Gichoya, Imon Banerjee, Hari Trivedi
We developed a bi-directional Long Short Term Memory (LSTM) Network that is able to use readily available insurance data (inpatient visits, outpatient visits, and drug prescriptions) to predict 30 day re-admission for any admitted patient, regardless of reason.
no code implementations • 13 May 2022 • Thiago Santos, Amara Tariq, Susmita Das, Kavyasree Vayalpati, Geoffrey H. Smith, Hari Trivedi, Imon Banerjee
Pathology text mining is a challenging task given the reporting variability and constant new findings in cancer sub-type definitions.
no code implementations • 6 Apr 2022 • Xiaoyuan Guo, Jiali Duan, Saptarshi Purkayastha, Hari Trivedi, Judy Wawira Gichoya, Imon Banerjee
While existing methods can be applied for class-wise retrieval (aka.
no code implementations • 8 Feb 2022 • Jiwoong J. Jeong, Brianna L. Vey, Ananth Reddy, Thomas Kim, Thiago Santos, Ramon Correa, Raman Dutt, Marina Mosunjac, Gabriela Oprea-Ilies, Geoffrey Smith, Minjae Woo, Christopher R. McAdams, Mary S. Newell, Imon Banerjee, Judy Gichoya, Hari Trivedi
Developing and validating artificial intelligence models in medical imaging requires datasets that are large, granular, and diverse.
no code implementations • 27 Dec 2021 • Xiaoyuan Guo, Judy Wawira Gichoya, Hari Trivedi, Saptarshi Purkayastha, Imon Banerjee
Given an internal dataset A as the base source, we first train anomaly detectors for each class of dataset A to learn internal distributions in an unsupervised way.
no code implementations • 16 Nov 2021 • Ramon Correa, Jiwoong Jason Jeong, Bhavik Patel, Hari Trivedi, Judy W. Gichoya, Imon Banerjee
The use of artificial intelligence (AI) in healthcare has become a very active research area in the last few years.
no code implementations • 21 Jul 2021 • Imon Banerjee, Ananth Reddy Bhimireddy, John L. Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, Matthew P Lungren, Lyle Palmer, Brandon J Price, Saptarshi Purkayastha, Ayis Pyrros, Luke Oakden-Rayner, Chima Okechukwu, Laleh Seyyed-Kalantari, Hari Trivedi, Ryan Wang, Zachary Zaiman, Haoran Zhang, Judy W Gichoya
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
no code implementations • 23 Jun 2020 • Imon Banerjee, Priyanshu Sinha, Saptarshi Purkayastha, Nazanin Mashhaditafreshi, Amara Tariq, Jiwoong Jeong, Hari Trivedi, Judy W. Gichoya
To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability.
1 code implementation • 16 Apr 2020 • Pradeeban Kathiravelu, Puneet Sharma, ASHISH SHARMA, Imon Banerjee, Hari Trivedi, Saptarshi Purkayastha, Priyanshu Sinha, Alexandre Cadrin-Chenevert, Nabile Safdar, Judy Wawira Gichoya
Executing machine learning (ML) pipelines in real-time on radiology images is hard due to the limited computing resources in clinical environments and the lack of efficient data transfer capabilities to run them on research clusters.