Authors: Pouria Yazdian Anari, Fiona Obiezu, Nathan Lay, Fatemeh Dehghani Firouzabadi, Aditi Chaurasia, Mahshid Golagha, Shiva Singh, Fatemeh Homayounieh, Aryan Zahergivar, Stephanie Harmon, Evrim Turkbey, Rabindra Gautam, Kevin Ma, Maria Merino, Elizabeth C. Jones, Mark W. Ball, W. Marston Linehan, Baris Turkbey, Ashkan A. Malayeri

Published on: February 08, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.05817

Summary

• What is new: The use of the latest YOLO V7 object detection method specifically for enhancing kidney detection in medical imaging, with a focus on renal cell carcinoma (RCC) and normal kidneys.
• Why this is important: Need for improved accuracy in kidney and tumor detection within medical imaging to support diagnostics.
• What the research proposes: A modified YOLO V7 model trained and tested on a comprehensive dataset of MRI scans with various subtypes of RCC and normal kidneys, employing a semi-supervised approach.
• Results: Achieved high accuracy with a PPV of up to 0.97, sensitivity up to 0.98, and mean average precision (mAP) up to 0.95.

Technical Details

Technological frameworks used: YOLO V7

Models used: Modified YOLO V7 for kidney detection

Data used: 5657 MRI scans for 1084 patients, including 326 patients with 1034 tumors

Potential Impact

Medical imaging, Health diagnostics companies, AI technology firms in healthcare, Radiology departments

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