1 code implementation • 14 Feb 2020 • Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y. Chen, Marzyeh Ghassemi
We demonstrate that TPR disparities exist in the state-of-the-art classifiers in all datasets, for all clinical tasks, and all subgroups.
Ranked #1 on Multi-Label Classification on ChestX-ray14
1 code implementation • 20 Mar 2021 • Haoran Zhang, Natalie Dullerud, Laleh Seyyed-Kalantari, Quaid Morris, Shalmali Joshi, Marzyeh Ghassemi
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
no code implementations • 31 Aug 2020 • Sina Akbarian, Laleh Seyyed-Kalantari, Farzad Khalvati, Elham Dolatabadi
To address this issue, we propose a teacher-student learning framework to transfer knowledge from a carefully pre-trained convolutional neural network (CNN) teacher to a student CNN.
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 • 4 May 2023 • Faiza Khan Khattak, Vallijah Subasri, Amrit Krishnan, Elham Dolatabadi, Deval Pandya, Laleh Seyyed-Kalantari, Frank Rudzicz
We cover the foundational concepts of general machine learning operations, describe the initial setup of MLHOps pipelines (including data sources, preparation, engineering, and tools).
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 • 7 Jun 2023 • Jacob-Junqi Tian, David Emerson, Sevil Zanjani Miyandoab, Deval Pandya, Laleh Seyyed-Kalantari, Faiza Khan Khattak
In this paper, we explore the use of soft-prompt tuning on sentiment classification task to quantify the biases of large language models (LLMs) such as Open Pre-trained Transformers (OPT) and Galactica language model.
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 • 4 Apr 2024 • Farnaz Kohankhaki, Jacob-Junqi Tian, David Emerson, Laleh Seyyed-Kalantari, Faiza Khan Khattak
This approach is widely used in bias quantification.