Dataset Information

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

ABSTRACT:

Purpose

The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs).

Methods

We developed an automatic RIQA system, named Swin-MCSFNet, utilizing 28,792 RIs from the EyeQ dataset, as well as 2000 images from the EyePACS dataset and an additional 1,000 images from the OIA-ODIR dataset. After preprocessing, including cropping black regions, data augmentation, and normalization, a Swin-MCSFNet classifier based on the Swin-Transformer for multiple color-space fusion was proposed to grade the quality of RIs. The generalizability of Swin-MCSFNet was validated across multiple data centers. Additionally, for enhanced interpretability, a Score-CAM-generated heatmap was applied to provide visual explanations.

Results

Experimental results reveal that the proposed Swin-MCSFNet achieves promising performance, yielding a micro-receiver operating characteristic (ROC) of 0.93 and ROC scores of 0.96, 0.81, and 0.96 for the "Good," "Usable," and "Reject" categories, respectively. These scores underscore the accuracy of RIQA based on Swin-MCSF in distinguishing among the three categories. Furthermore, heatmaps generated across different RIQA classification scores and various color spaces suggest that regions in the retinal images from multiple color spaces contribute significantly to the decision-making process of the Swin-MCSFNet classifier.

Conclusions

Our study demonstrates that the proposed Swin-MCSFNet outperforms other methods in experiments conducted on multiple datasets, as evidenced by the superior performance metrics and insightful Score-CAM heatmaps.

Translational relevance

This study constructs a new retinal image quality evaluation system, which will contribute to the subsequent research of retinal images.

SUBMITTER: Huang C

PROVIDER: S-EPMC10996994 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

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Publications

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Huang Chengcheng C Jiang Yukang Y Yang Xiaochun X Wei Chiyu C Chen Hongyu H Xiong Weixue W Lin Henghui H Wang Xueqin X Tian Ting T Tan Haizhu H

Translational vision science & technology 20240401 4

<h4>Purpose</h4>The assessment of retinal image (RI) quality holds significant importance in both clinical trials and large datasets, because suboptimal images can potentially conceal early signs of diseases, thereby resulting in inaccurate medical diagnoses. This study aims to develop an automatic method for Retinal Image Quality Assessment (RIQA) that incorporates visual explanations, aiming to comprehensively evaluate the quality of retinal fundus images (RIs).<h4>Methods</h4>We developed an ...[more]

PMID: 38568606

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Project description:BackgroundUltrasound imaging is pivotal for point of care non-invasive diagnosis of musculoskeletal (MSK) injuries. Notably, MSK ultrasound demands a higher level of operator expertise compared to general ultrasound procedures, necessitating thorough checks on image quality and precise categorization of each image. This need for skilled assessment highlights the importance of developing supportive tools for quality control and categorization in clinical settings. To address these challenges, we aim to develop and evaluate an automated system designed to assess whether an ultrasound image meets established standards and to identify its specific category, thereby assisting clinicians in improving diagnostic efficiency and accuracy in MSK ultrasound.MethodsWe proposed a two-stage system built upon a Swin Transformer-based Unet model. Initially, an ultrasound image of the elbow is categorized, following which its quality is evaluated to determine if it meets the established standard criteria set by experienced radiologists.ResultsThe proposed method surpassed other approaches in performance, with the Swin Transformer, serving as the backbone, outperforming conventional convolutional neural network (CNN) models. Additionally, when utilizing the same backbone, the two-stage model exceeded the capabilities of the single-stage model. Notably, the accuracy of our model exceeded 97%, with each category achieving over 95%. Additionally, processing time was significantly reduced from over 16 minutes to less than 15 seconds. Furthermore, this model reduces the workload for experienced radiologists, demonstrating its efficiency and effectiveness. This allows human experts to focus more on rare and challenging cases that machine learning struggles to solve, and to create high-quality labels for the models to learn from.ConclusionsWe have introduced an elbow ultrasound image recognition system leveraging the Swin Transformer, achieving competitive outcomes in both categorization and standard verification. This approach significantly reduces diagnostic time, allowing radiologists to focus more on scientific validation rather than image analysis.

Dataset Information

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Purpose

Methods

Results

Conclusions

Translational relevance

Publications

Enhancing Retinal Fundus Image Quality Assessment With Swin-Transformer-Based Learning Across Multiple Color-Spaces.

Similar Datasets

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