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Certainty and OOD Detection in Medical Images and Multiple Sclerosis
Manage episode 444738221 series 3605861
المحتوى المقدم من Brian Carter. يتم تحميل جميع محتويات البودكاست بما في ذلك الحلقات والرسومات وأوصاف البودكاست وتقديمها مباشرة بواسطة Brian Carter أو شريك منصة البودكاست الخاص بهم. إذا كنت تعتقد أن شخصًا ما يستخدم عملك المحمي بحقوق الطبع والنشر دون إذنك، فيمكنك اتباع العملية الموضحة هنا https://ar.player.fm/legal.
This research paper investigates the challenges of detecting Out-of-Distribution (OOD) inputs in medical image segmentation tasks, particularly in the context of Multiple Sclerosis (MS) lesion segmentation. The authors propose a novel evaluation framework that uses 14 different sources of OOD, including synthetic artifacts and real-world variations in imaging data. They examine various uncertainty quantification (UQ) methods, including Maximum Softmax Probability (MSP), Monte Carlo dropout (MC dropout), Deep Ensemble (DE), and Deterministic Uncertainty Methods (DUM). Their findings demonstrate that multiclass segmentation models, which segment both lesions and anatomical regions, significantly outperform binary models in detecting OOD inputs. This suggests that incorporating anatomical information helps the models better understand the context of the input images and recognize abnormalities. The study also highlights the potential of DUM for efficient and effective OOD detection in medical image segmentation.
Read more: https://arxiv.org/pdf/2211.05421
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