SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

Ma, Chao

doi:10.36227/techrxiv.16823236

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Both SimCLR and MAE have shown their efficacy in various computer vision tasks, such as [41], [42]. SimCLR, with its contrastive learning framework, excels at capturing the structural details of images, while MAE is particularly adept at learning fine-grained details.…”

Section: B Neural Network Pre-trainingmentioning

confidence: 99%

Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation Through Multiscale Satellite Imagery and Self-Supervised Cross-Matching

Liang,

Zulu,

Xing

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Traffic accidents threaten human lives and impose substantial financial burdens annually. Accurate estimation of accident fatal crash risk is crucial for enhancing road safety and saving lives. This paper proposes an innovative approach that utilizes multi-scale satellite imagery and self-supervised learning for fatal crash risk estimation. By integrating multi-scale imagery, our network captures diverse features at different scales, encompassing observations of surrounding environmental factors in low-resolution images that cover larger areas and learning detailed ground-level information from high-resolution images. One advantage of our work is its sole reliance on satellite imagery data, making it an efficient and practical solution, especially when other data modalities are unavailable. With the ability to accurately estimate fatal crash risk, our method exhibits a potential for enhancing road safety, optimizing infrastructure planning, preventing accidents, and ultimately saving lives.

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Section: B Neural Network Pre-trainingmentioning

confidence: 99%

Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation Through Multiscale Satellite Imagery and Self-Supervised Cross-Matching

Liang,

Zulu,

Xing

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…The fundamental difference between a supervised and unsupervised paradigms is the incorporation of pseudo gold standard in the form of another observation which is similar to original datasets whose segmentation needs to be determined. Such a pseudo-observation is typically adapted for training the model, exactly the way the gold standard does [164,[263][264][265][266].…”

Section: A Short Note On Unsupervised Unet Paradigmmentioning

confidence: 99%

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

Suri¹,

Bhagawati

Agarwal³

et al. 2023

IEEE Access

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Background & Motivation: Biomedical image segmentation (BIS) task is challenging due to the variations in organ types, position, shape, size, scale, orientation, and image contrast. Conventional methods lack accurate and automated designs. Artificial intelligence (AI)-based UNet has recently dominated BIS. This is the first review of its kind that microscopically addressed UNet types by complexity, stratification of UNet by its components, addressing UNet in vascular vs. non-vascular framework, the key to segmentation challenge vs. UNet-based architecture, and finally interfacing the three facets of AI, the pruning, the explainable AI (XAI), and the AI-bias. Methods: PRISMA was used to select 267 UNet-based studies. Five classes were identified and labeled as conventional UNet, superior UNet, attention-channel UNet, hybrid UNet, and ensemble UNet. We discovered 81 variations of UNet by considering six kinds of components, namely encoder, decoder, skip connection, bridge network, loss function, and their combination. Vascular vs. non-vascular UNet architecture was compared. AP(ai)Bias 2.0-UNet was identified in these UNet classes based on (i) attributes of UNet architecture and its performance, (ii) explainable AI (XAI), and, (iii) pruning (compression). Five bias methods such as (i) ranking, (ii) radial, (iii) regional area, (iv) PROBAST, and (v) ROBINS-I were applied and compared using a Venn diagram. Findings and Conclusions: Vascular and non-vascular UNet systems dominated with sUNet classes with attention. The majority of the studies suffered from low interest in XAI and pruning strategies. None of the UNet models qualified to be bias-free. There is a need to move from paper-to-practice paradigms for clinical evaluation and settings. INDEX TERMS Image segmentation; vascular; non-vascular; UNet classes; UNet variations; UNetcomponents; explainable AI; pruning; bias.

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SSL-Unet: A Self-Supervised Learning Strategy Base on U-Net for Retinal Vessel Segmentation

Cited by 2 publications

References 6 publications

Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation Through Multiscale Satellite Imagery and Self-Supervised Cross-Matching

Unveiling Roadway Hazards: Enhancing Fatal Crash Risk Estimation Through Multiscale Satellite Imagery and Self-Supervised Cross-Matching

UNet Deep Learning Architecture for Segmentation of Vascular and Non-Vascular Images: A Microscopic Look at UNet Components Buffered With Pruning, Explainable Artificial Intelligence, and Bias

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