The paper titled “Enhanced Small Liver Lesion Detection and Segmentation Using a Size-focused Multi-model Approach in CT Scans”, co-authored by VinBrain scientists (Applied Science Division) and researchers from the University of California, USA, has recently been accepted at the 15th International Workshop on Machine Learning in Medical Imaging (MLMI 2024). Additionally, the paper “A Multi-Phase Multi-Graph Approach for Focal Liver Lesion Classification on CT Scans”, published and presented at ACCV 2024, will be applied to DrAid™ CT Liver Cancer, further enhancing its precision and comprehensiveness.
This paper introduces a groundbreaking approach that helps doctors detect small focal liver lesions, preventing critical findings from being missed. It is the second consecutive study within two months to be accepted at some of the most prestigious conferences in the field of Artificial Intelligence.
Machine Learning in Medical Imaging (MLMI 2024) is the 15th in a series of workshops on this topic in conjunction with MICCAI 2024 as a half-day event on October 6, 2024. This workshop focuses on major trends and challenges in this area, and it presents original work aimed to identify new cutting-edge techniques and their applications in medical imaging.
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