RedCircle VERIFY_TOKEN_81129f4a-601e-4e98-858f-43ce443361f7 -- Rendered At Fri, 16 Oct 2026 10:04:22 +0000 Papers by Abhijeet https://redcircle.com/shows/papers-by-abhijeet en-US All rights reserved. Abhijeet Parida A Podcast on AI + Healthcare Innovation Welcome to *Papers by Abhijeet* , where cutting-edge research meets clear, compelling storytelling. Each episode distills a peer-reviewed paper, exploring the transformative intersections of artificial intelligence and healthcare. Generated with NotebookLM and delivered in an accessible format, these episodes are designed to make complex ideas understandable — whether you're a clinician, researcher, policymaker, or simply curious about the future of health and technology. Tune in to stay informed, inspired, and ahead of the curve. 81129f4a-601e-4e98-858f-43ce443361f7 A Podcast on AI + Healthcare Innovation

Welcome to Papers by Abhijeet, where cutting-edge research meets clear, compelling storytelling. Each episode distills a peer-reviewed paper, exploring the transformative intersections of artificial intelligence and healthcare.

Generated with NotebookLM and delivered in an accessible format, these episodes are designed to make complex ideas understandable — whether you're a clinician, researcher, policymaker, or simply curious about the future of health and technology.

Tune in to stay informed, inspired, and ahead of the curve.

]]> episodic yes Abhijeet Parida abhijeetfb1295@gmail.com false full Efficient Organ Segmentation with Minimal Bounding Boxes Efficient Organ Segmentation with Minimal Bounding Boxes Abhijeet Parida This academic paper presents a novel approach to semantic segmentation in medical imaging, specifically focusing on organ identification using a limited amount of labeled data. The authors introduce a method that significantly reduces the need for extensive, pixel-level annotations by leveraging bounding box annotations for semi-supervised few-shot learning. Their proposed U-Net-based unbranched architecture utilizes prototype learning and a combined loss function to achieve fast 3D segmentation, demonstrating improved performance with fewer annotations compared to traditional supervised methods. The research highlights the potential of few-shot learning to address the challenge of data scarcity and the high cost of expert annotation in the medical domain.


CITE:

Parida A, Tran A, Navab N, Albarqouni S. Learn to segment organs with a few bounding boxes. arXiv preprint arXiv:1909.07809. 2019 Sep 17. https://arxiv.org/abs/1909.07809

]]> <p>This academic paper presents a novel approach to semantic segmentation in medical imaging, specifically focusing on organ identification using a limited amount of labeled data. The authors introduce a method that significantly reduces the need for extensive, pixel-level annotations by leveraging bounding box annotations for semi-supervised few-shot learning. Their proposed U-Net-based unbranched architecture utilizes prototype learning and a combined loss function to achieve fast 3D segmentation, demonstrating improved performance with fewer annotations compared to traditional supervised methods. The research highlights the potential of few-shot learning to address the challenge of data scarcity and the high cost of expert annotation in the medical domain.</p><p><br></p><p>CITE:</p><p><span>Parida A, Tran A, Navab N, Albarqouni S. Learn to segment organs with a few bounding boxes. arXiv preprint arXiv:1909.07809. 2019 Sep 17. </span>https://arxiv.org/abs/1909.07809</p> 9486025f-25ef-437a-9775-7cdfaba8abce https://redcircle.com/shows/81129f4a-601e-4e98-858f-43ce443361f7/episodes/20477721-2e38-419a-8034-d486e62c62c8 Tue, 22 Jul 2025 01:12:14 +0000 653 false