1 code implementation • 6 Feb 2024 • Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Joseph Y. Lo
First, we examined model tolerance for noisy data by incrementally increasing error in the labels within the training data.
no code implementations • 23 Feb 2022 • Fakrul Islam Tushar, Vincent M. D'Anniballe, Geoffrey D. Rubin, Ehsan Samei, Joseph Y. Lo
Despite the potential of weakly supervised learning to automatically annotate massive amounts of data, little is known about its limitations for use in computer-aided diagnosis (CAD).
1 code implementation • 5 Feb 2021 • Vincent M. D'Anniballe, Fakrul Islam Tushar, Khrystyna Faryna, Songyue Han, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo
Pre-trained models outperformed random initialization across all diseases.
1 code implementation • 31 Oct 2020 • Anindo Saha, Fakrul I. Tushar, Khrystyna Faryna, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Geoffrey D. Rubin, Joseph Y. Lo
Second, segmentation and classification models are connected with two different feature aggregation strategies to enhance the classification performance.
1 code implementation • 3 Aug 2020 • Fakrul Islam Tushar, Vincent M. D'Anniballe, Rui Hou, Maciej A. Mazurowski, Wanyi Fu, Ehsan Samei, Geoffrey D. Rubin, Joseph Y. Lo
Purpose: To design multi-disease classifiers for body CT scans for three different organ systems using automatically extracted labels from radiology text reports. Materials & Methods: This retrospective study included a total of 12, 092 patients (mean age 57 +- 18; 6, 172 women) for model development and testing (from 2012-2017).