Titlebook: Data Engineering in Medical Imaging; Second MICCAI Worksh Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2025 The Editor

被告

In this paper, we present the EndoDepth benchmark, an evaluation framework designed to assess the robustness of monocular depth prediction models in endoscopic scenarios. Unlike traditional datasets, the EndoDepth benchmark incorporates common challenges encountered during endoscopic procedures. We

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assent

ance the effectiveness of real-time processing in AI systems for ultrasound image analysis, a pre-processing step to detect liver views is essential, as many abdominal ultrasound images do not include the liver. In this paper, we introduce a method for efficient liver view classification in ultrasou

eardrum

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Artefakte biologischen Ursprungs,etinal images to predict biomarkers for non-retinal diseases such as cardiovascular disease and chronic kidney disease has shown promise. However, despite the success of utilizing retinal images, significant challenges remain. One major issue is the limited availability of retinal images with linked

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disparage

Steuern: Gemeinwohl und Gerechtigkeit,such a problem is the lack of annotated data. Endoscopic annotations necessitate the specialist knowledge of expert endoscopists, and hence the difficulty of organizing arises along with tremendous costs in time and budget. To address this problem, we investigate an active learning paradigm to reduc

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