no code implementations • 22 Feb 2024 • Daniel Capellán-Martín, Abhijeet Parida, Juan J. Gómez-Valverde, Ramon Sanchez-Jacob, Pooneh Roshanitabrizi, Marius G. Linguraru, María J. Ledesma-Carbayo, Syed M. Anwar
We demonstrate improvements in TB detection performance ($\sim$12. 7% and $\sim$13. 4% top AUC/AUPR gains in adults and children, respectively) when conducting self-supervised pre-training when compared to fully-supervised (i. e., non pre-trained) ViT models, achieving top performances of 0. 959 AUC and 0. 962 AUPR in adult TB detection, and 0. 697 AUC and 0. 607 AUPR in zero-shot pediatric TB detection.
no code implementations • 6 Feb 2024 • Abhijeet Parida, Zhifan Jiang, Roger J. Packer, Robert A. Avery, Syed M. Anwar, Marius G. Linguraru
However, benchmarking the effectiveness of harmonization techniques has been a challenge due to the lack of widely available standardized datasets with ground truths.
no code implementations • 21 Aug 2023 • Abhijeet Parida, Zhifan Jiang, Syed Muhammad Anwar, Nicholas Foreman, Nicholas Stence, Michael J. Fisher, Roger J. Packer, Robert A. Avery, Marius George Linguraru
To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization.
no code implementations • 23 Nov 2022 • Syed Muhammad Anwar, Abhijeet Parida, Sara Atito, Muhammad Awais, Gustavo Nino, Josef Kitler, Marius George Linguraru
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
Ranked #1 on Semantic Segmentation on Montgomery County X-ray Set
no code implementations • 19 Mar 2021 • Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim, Nassir Navab
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.
no code implementations • 18 Mar 2020 • Abhijeet Parida, Aadhithya Sankar, Rami Eisawy, Tom Finck, Benedikt Wiestler, Franz Pfister, Julia Moosbauer
High-quality labeled data is essential to successfully train supervised machine learning models.
no code implementations • 17 Sep 2019 • Abhijeet Parida, Arianne Tran, Nassir Navab, Shadi Albarqouni
Semantic segmentation is an import task in the medical field to identify the exact extent and orientation of significant structures like organs and pathology.