RTNet: Relation Transformer Network for Diabetic Retinopathy Multi-Lesion Segmentation [PDF]
IEEE Transactions on Medical Imaging, 2022 Automatic diabetic retinopathy (DR) lesions segmentation makes great sense of assisting ophthalmologists in diagnosis. Although many researches have been conducted on this task, most prior works paid too much attention to the designs of networks instead ...
Shiqi Huang +4 more
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Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC) [PDF]
IEEE International Symposium on Biomedical Imaging, 2016 This article describes the design, implementation, and results of the latest installment of the dermoscopic image analysis benchmark challenge. The goal is to support research and development of algorithms for automated diagnosis of melanoma, the most ...
D. Gutman +6 more
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Exploring Uncertainty Measures in Deep Networks for Multiple Sclerosis Lesion Detection and Segmentation [PDF]
International Conference on Medical Image Computing and Computer-Assisted Intervention, 2018 Deep learning networks have recently been shown to outperform other segmentation methods on various public, medical-image challenge datasets, particularly on metrics focused on large pathologies.
T. Nair +3 more
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Skin Lesion Analysis towards Melanoma Detection Using Deep Learning Network [PDF]
Italian National Conference on Sensors, 2017 Skin lesions are a severe disease globally. Early detection of melanoma in dermoscopy images significantly increases the survival rate. However, the accurate recognition of melanoma is extremely challenging due to the following reasons: low contrast ...
Yuexiang Li, L. Shen
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A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification [PDF]
IEEE Transactions on Medical Imaging, 2019 Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential ...
Yutong Xie +3 more
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Skin Lesion Segmentation in Dermoscopic Images With Ensemble Deep Learning Methods
IEEE Access, 2020 Early detection of skin cancer, particularly melanoma, is crucial to enable advanced treatment. Due to the rapid growth in the number of skin cancers, there is a growing need of computerised analysis for skin lesions.
M. Goyal +4 more
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Journal of Medical Imaging, 2018 . Extracting, harvesting, and building large-scale annotated radiological image datasets is a greatly important yet challenging problem. Meanwhile, vast amounts of clinical annotations have been collected and stored in hospitals’ picture archiving and ...
K. Yan, Xiaosong Wang, Le Lu, R. Summers
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Post-stroke deficit prediction from lesion and indirect structural and functional disconnection.
Brain : a journal of neurology, 2020 Behavioural deficits in stroke reflect both structural damage at the site of injury, and widespread network dysfunction caused by structural, functional, and metabolic disconnection.
A. Salvalaggio +4 more
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The Generalized Contrast-to-Noise Ratio: A Formal Definition for Lesion Detectability
IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control, 2019 In the last 30 years, the contrast-to-noise ratio (CNR) has been used to estimate the contrast and lesion detectability in ultrasound images. Recent studies have shown that the CNR cannot be used with modern beamformers, as dynamic range alterations can ...
Alfonso Rodriguez-Molares +6 more
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Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
Diagnostics, 2019 Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts
H. Ünver, Enes Ayan
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