Titlebook: Data Engineering in Medical Imaging; First MICCAI Worksho Binod Bhattarai,Sharib Ali,Danail Stoyanov Conference proceedings 2023 The Editor

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Data Engineering in Medical Imaging978-3-031-44992-5Series ISSN 0302-9743 Series E-ISSN 1611-3349

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A. Berlin,A. E. Bennett,W. J. Hunteraluation and treatment guidance. This paper presents a deep learning (DL) classification pipeline for quantifying clinical information from digital core-needle biopsy (CNB) images, with one step less than existing methods. A publicly available dataset of 1058 patients was used to evaluate the perfor

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https://doi.org/10.1007/978-94-009-3197-8rce to develop a better performing deep learning model from scratch. Transfer learning with networks pre-trained on ImageNet is commonly applied to address this problem. FDSL techniques have been recently investigated as an alternative solution to ImageNet based approaches. In the FDSL setting, netw

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A European Overview of the Health Care Scenee effectiveness of these algorithms is contingent upon the availability and organization of high-quality medical imaging datasets. Traditional Digital Imaging and Communications in Medicine (DICOM) data management systems are inadequate for handling the scale and complexity of data required to be fa

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Light-Induced Changes in the Skin of the Lidscope navigates the anatomy. However, this task is made challenging by the lack of discriminative localisation landmarks throughout the colon. While standard navigation techniques rely on sparse point landmarks or dense pixel registration, we propose edges as a more natural visual landmark to charac

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Phototoxic Changes in the Retinaing data can be difficult to obtain, or of limited size. Procedural generation of data allows for large datasets to be rapidly generated and automatically labelled, while also randomising relevant parameters within the simulation to provide a wide variation in models and textures used in the scene..

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Clinical Light Damage to the Eyeactors. For natural image classification training with noisy labeled data, model initialization with contrastive self-supervised pretrained weights has shown to reduce feature corruption and improve classification performance. However, no works have explored: i) how other self-supervised approaches,