no code implementations • 10 Apr 2024 • Pranav Kulkarni, Adway Kanhere, Harshita Kukreja, Vivian Zhang, Paul H. Yi, Vishwa S. Parekh
Generative Adversarial Network (GAN)-based synthesis of fat suppressed (FS) MRIs from non-FS proton density sequences has the potential to accelerate acquisition of knee MRIs.
1 code implementation • 22 Mar 2024 • Pranav Kulkarni, Adway Kanhere, Dharmam Savani, Andrew Chan, Devina Chatterjee, Paul H. Yi, Vishwa S. Parekh
Curating annotations for medical image segmentation is a labor-intensive and time-consuming task that requires domain expertise, resulting in "narrowly" focused deep learning (DL) models with limited translational utility.
no code implementations • 12 Feb 2024 • Ghada Zamzmi, Kesavan Venkatesh, Brandon Nelson, Smriti Prathapan, Paul H. Yi, Berkman Sahiner, Jana G. Delfino
Conclusion: We propose a framework for OOD detection and drift monitoring that is agnostic to data, modality, and model.
1 code implementation • 8 Feb 2024 • Pranav Kulkarni, Andrew Chan, Nithya Navarathna, Skylar Chan, Paul H. Yi, Vishwa S. Parekh
The proliferation of artificial intelligence (AI) in radiology has shed light on the risk of deep learning (DL) models exacerbating clinical biases towards vulnerable patient populations.
3 code implementations • 1 Jul 2023 • Pranav Kulkarni, Adway Kanhere, Eliot Siegel, Paul H. Yi, Vishwa S. Parekh
We propose MIST, an open-source framework to operationalize progressive resolution for streaming medical images at multiple resolutions from a single high-resolution copy.
no code implementations • 24 May 2023 • Pranav Kulkarni, Sean Garin, Adway Kanhere, Eliot Siegel, Paul H. Yi, Vishwa S. Parekh
As the adoption of Artificial Intelligence (AI) systems within the clinical environment grows, limitations in bandwidth and compute can create communication bottlenecks when streaming imaging data, leading to delays in patient care and increased cost.
1 code implementation • 12 May 2023 • Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh
We demonstrate that Text2Cohort can enable researchers to discover and curate cohorts on IDC with high levels of accuracy using natural language in a more intuitive and user-friendly way.
no code implementations • 10 Mar 2023 • Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh
Numerous large-scale chest x-ray datasets have spearheaded expert-level detection of abnormalities using deep learning.
1 code implementation • 17 Jan 2023 • Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh
The release of numerous chest x-ray datasets has spearheaded the development of deep learning models with expert-level performance.
no code implementations • 17 Jan 2023 • Adway U. Kanhere, Pranav Kulkarni, Paul H. Yi, Vishwa S. Parekh
Segmentation is one of the most primary tasks in deep learning for medical imaging, owing to its multiple downstream clinical applications.
1 code implementation • 29 Nov 2022 • Jacopo Teneggi, Paul H. Yi, Jeremias Sulam
We find that strong supervision (i. e., learning with local image-level annotations) and weak supervision (i. e., learning with only global examination-level labels) achieve comparable performance in examination-level hemorrhage detection (the task of selecting the images in an examination that show signs of hemorrhage) as well as in image-level hemorrhage detection (highlighting those signs within the selected images).
no code implementations • 11 Nov 2022 • Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh
Chest X-ray (CXR) datasets hosted on Kaggle, though useful from a data science competition standpoint, have limited utility in clinical use because of their narrow focus on diagnosing one specific disease.