Endoscopic DCR Versus External DCR

Bhandary, Rukma; Kudva, Ajay; Tanthry, Deepalakshmi; P, Devan P; Mahesh, Seema; Sowmya, Sowmya; K, Sudhir Hegde

doi:10.14260/jemds/2015/856

Cited by 1 publication

(2 citation statements)

References 10 publications

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“…In the fold of medical imaging, three main tracks are receiving more attention, such as diagnosis, segmentation, and survival prediction. The image data modalities include CT [130] , MRI [131] , X-ray [132] , Ultrasound [133] , Dermoscopy [55] , Ophthalmology [134] , whole slide tissue images (WSI) [60] , etc. In recent years, learning in medical images has changed from traditional heuristic learning to learning-based learning, which means that new learning methods can obtain essential information from a large number of unlabelled medical images [135] .…”

Section: Medical Images In Pre-trainingmentioning

confidence: 99%

“…The extensive experiments demonstrate that the LNE is positive for exploiting the association of information temporal and spatial to reveal the impact of neurodegenerative disorders. Ren et al [131] presented a local and multiscale spatial-temporal representation learning method for pre-training on longitudinal MRI imaging datasets, while they proposed various regularisations for avoiding collapsing when extending to multi-scale spatial-temporal representations. They evaluated the improvement in longitudinal neurodegenerative adult MRI and developing in-fant′s brain MRI for segmentation tasks.…”

Section: Longitudinal Images Datamentioning

confidence: 99%

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Pre-training in Medical Data: A Survey

et al. 2023

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Medical data refers to health-related information associated with regular patient care or as part of a clinical trial program. There are many categories of such data, such as clinical imaging data, bio-signal data, electronic health records (EHR), and multi-modality medical data. With the development of deep neural networks in the last decade, the emerging pre-training paradigm has become dominant in that it has significantly improved machine learning methods’ performance in a data-limited scenario. In recent years, studies of pre-training in the medical domain have achieved significant progress. To summarize these technology advancements, this work provides a comprehensive survey of recent advances for pre-training on several major types of medical data. In this survey, we summarize a large number of related publications and the existing benchmarking in the medical domain. Especially, the survey briefly describes how some pre-training methods are applied to or developed for medical data. From a data-driven perspective, we examine the extensive use of pre-training in many medical scenarios. Moreover, based on the summary of recent pre-training studies, we identify several challenges in this field to provide insights for future studies.

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