Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation

Wu, Congzhong; Sun, Jun; Wang, Jing; Xu, Lu; Shu, Zhan

doi:10.1007/s11633-020-1277-0

Cited by 12 publications

(3 citation statements)

References 31 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%

“…Given that current radiologists′ professional skills and knowledge are not very reliable, many abnormal ultrasound images of fatty liver cannot be well diagnosed, leading to the development of fatty liver into a fatal chronic disease. In order to improve the accuracy of ultrasound image classification, Reddy et al [133,149] proposed the convolutional neural network combined with transfer learning (VGG16 pre-train) to analyse and identify whether there is fatty liver. At the same time, these two articles compare the ordinary CNN without pre-training and other non-deep learning methods.…”

Section: Ultrasoundmentioning

confidence: 99%

“…To leverage the unlabelled bio-signals, like [16], Thin- X-ray [133,149] Ultrasound [132,150] Segmentation Abdnomial [44,48,153,155] Ultrasound [157,[159][160][161] Comprehensive [163,164,167] Survival prediction WSI [58,60,171,172,174] sungnoen et al [35] designed auto-encoder networks training bio-signal representations and then clustering features. In recent years, the emerging family of self-supervised (contrastive) learning methods has been applied to bio-signals data [186-192, 199, 202-204] .…”

Section: Disease Diagnosismentioning

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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Section: Medical Images In Pre-trainingmentioning

confidence: 99%

Section: Ultrasoundmentioning

confidence: 99%

Section: Disease Diagnosismentioning

confidence: 99%

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Pre-training in Medical Data: A Survey

et al. 2023

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Improved U-Net Network for Segmentation on Femur Images

Zhao

Han

et al. 2022

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery

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Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

Gao²,

Dai³

2022

Mach. Intell. Res.

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Seeing through dense occlusions and reconstructing scene images is an important but challenging task. Traditional frame-based image de-occlusion methods may lead to fatal errors when facing extremely dense occlusions due to the lack of valid information available from the limited input occluded frames. Event cameras are bio-inspired vision sensors that record the brightness changes at each pixel asynchronously with high temporal resolution. However, synthesizing images solely from event streams is ill-posed since only the brightness changes are recorded in the event stream, and the initial brightness is unknown. In this paper, we propose an event-enhanced multi-modal fusion hybrid network for image de-occlusion, which uses event streams to provide complete scene information and frames to provide color and texture information. An event stream encoder based on the spiking neural network (SNN) is proposed to encode and denoise the event stream efficiently. A comparison loss is proposed to generate clearer results. Experimental results on a large-scale event-based and frame-based image de-occlusion dataset demonstrate that our proposed method achieves state-of-the-art performance.

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Encoding-decoding Network With Pyramid Self-attention Module For Retinal Vessel Segmentation

Cited by 12 publications

References 31 publications

Pre-training in Medical Data: A Survey

Pre-training in Medical Data: A Survey

Improved U-Net Network for Segmentation on Femur Images

Image De-occlusion via Event-enhanced Multi-modal Fusion Hybrid Network

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