Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework

Erjian, Guo,; Fu, Huazhu; Zhou, Luping; Xu, Dong

doi:10.1109/tmi.2023.3247783

Cited by 6 publications

(2 citation statements)

References 43 publications

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“…Due to the success of CL (Wu et al 2018;Chen et al 2020Chen et al , 2021Chen et al , 2022cGuo et al 2023), numerous efforts have been made to improve the robustness of classification tasks by harnessing the advantages of CL. For instance, (Zheltonozhskii et al 2022) employed CL as a pre-training technique for their classification model.…”

Section: Contrastive Learning (Cl)mentioning

confidence: 99%

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

Liu,

Chen,

Zhou

et al. 2024

AAAI

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Conventional Federated Domain Adaptation (FDA) approaches usually demand an abundance of assumptions, which makes them significantly less feasible for real-world situations and introduces security hazards. This paper relaxes the assumptions from previous FDAs and studies a more practical scenario named Universal Federated Domain Adaptation (UFDA). It only requires the black-box model and the label set information of each source domain, while the label sets of different source domains could be inconsistent, and the target-domain label set is totally blind. Towards a more effective solution for our newly proposed UFDA scenario, we propose a corresponding methodology called Hot-Learning with Contrastive Label Disambiguation (HCLD). It particularly tackles UFDA's domain shifts and category gaps problems by using one-hot outputs from the black-box models of various source domains. Moreover, to better distinguish the shared and unknown classes, we further present a cluster-level strategy named Mutual-Voting Decision (MVD) to extract robust consensus knowledge across peer classes from both source and target domains. Extensive experiments on three benchmark datasets demonstrate that our method achieves comparable performance for our UFDA scenario with much fewer assumptions, compared to previous methodologies with comprehensive additional assumptions.

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Section: Contrastive Learning (Cl)mentioning

confidence: 99%

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

Liu,

Chen,

Zhou

et al. 2024

AAAI

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“…The problem of improving retinal fundus picture qualitywhich is essential for boosting clinical observations and lowering the possibility of misdiagnosis-was taken up by the researchers in [24]. They put forth a teacher-student system that is end-to-end optimized and designed to provide simultaneous image augmentation and domain adaptability.…”

Section: Review Of Existing Modelsmentioning

confidence: 99%

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Jagadale Sachin Mohan

2023

IJRITCC

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The burgeoning field of retinal image processing is critical in facilitating early diagnosis and treatment of retinal diseases, which are amongst the leading causes of vision impairment globally. Despite rapid advancements, existing machine learning models for retinal image processing are characterized by significant limitations, including disparities in pre-processing, segmentation, and classification methodologies, as well as inconsistencies in post-processing operations. These limitations hinder the realization of accurate, reliable, and clinically relevant outcomes. This paper provides an in-depth statistical review of extant machine learning models used in retinal image processing, meticulously comparing them based on their internal operating characteristics and performance levels. By adopting a robust analytical approach, our review delineates the strengths and weaknesses of current models, offering comprehensive insights that are instrumental in guiding future research and development in this domain. Furthermore, this review underscores the potential clinical impacts of these models, highlighting their pivotal role in enhancing diagnostic accuracy, prognostic assessments, and therapeutic interventions for retinal disorders. In conclusion, our work not only bridges the existing knowledge gap in the literature but also paves the way for the evolution of more sophisticated and clinically-aligned retinal image processing models, ultimately contributing to improved patient outcomes and advancements in ophthalmic care.

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Swarm Based Enhancement Optimization Method for Image Enhancement for Diabetic Retinopathy Detection

Vinodhini,

Ramachandran

2024

Communications in Computer and Information Science

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Bridging Synthetic and Real Images: A Transferable and Multiple Consistency Aided Fundus Image Enhancement Framework

Cited by 6 publications

References 43 publications

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

UFDA: Universal Federated Domain Adaptation with Practical Assumptions

An In-Depth Statistical Review of Retinal Image Processing Models from a Clinical Perspective

Swarm Based Enhancement Optimization Method for Image Enhancement for Diabetic Retinopathy Detection

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