OMIA@MICCAI Pub Date : 2023-07-25 DOI: 10.1007/978-3-031-44013-7_14

T. Emre, Marzieh Oghbaie, A. Chakravarty, Antoine Rivail, Sophie Riedl, Julia Mai, H. Scholl, S. Sivaprasad, D. Rueckert, A. Lotery, U. Schmidt-Erfurth, Hrvoje Bogunovi'c

引用次数: 0

QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models QuickQual:轻量级，方便的视网膜图像质量评分与现成的预训练模型

OMIA@MICCAI Pub Date : 2023-07-25 DOI: 10.48550/arXiv.2307.13646

Justin Engelmann, A. Storkey, M. Bernabeu

{"title":"QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models","authors":"Justin Engelmann, A. Storkey, M. Bernabeu","doi":"10.48550/arXiv.2307.13646","DOIUrl":"https://doi.org/10.48550/arXiv.2307.13646","url":null,"abstract":"Image quality remains a key problem for both traditional and deep learning (DL)-based approaches to retinal image analysis, but identifying poor quality images can be time consuming and subjective. Thus, automated methods for retinal image quality scoring (RIQS) are needed. The current state-of-the-art is MCFNet, composed of three Densenet121 backbones each operating in a different colour space. MCFNet, and the EyeQ dataset released by the same authors, was a huge step forward for RIQS. We present QuickQual, a simple approach to RIQS, consisting of a single off-the-shelf ImageNet-pretrained Densenet121 backbone plus a Support Vector Machine (SVM). QuickQual performs very well, setting a new state-of-the-art for EyeQ (Accuracy: 88.50% vs 88.00% for MCFNet; AUC: 0.9687 vs 0.9588). This suggests that RIQS can be solved with generic perceptual features learned on natural images, as opposed to requiring DL models trained on large amounts of fundus images. Additionally, we propose a Fixed Prior linearisation scheme, that converts EyeQ from a 3-way classification to a continuous logistic regression task. For this task, we present a second model, QuickQual MEga Minified Estimator (QuickQual-MEME), that consists of only 10 parameters on top of an off-the-shelf Densenet121 and can distinguish between gradable and ungradable images with an accuracy of 89.18% (AUC: 0.9537). Code and model are available on GitHub: https://github.com/justinengelmann/QuickQual . QuickQual is so lightweight, that the entire inference code (and even the parameters for QuickQual-MEME) is already contained in this paper.","PeriodicalId":261989,"journal":{"name":"OMIA@MICCAI","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126926207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mapping the Ocular Surface from Monocular Videos with an Application to Dry Eye Disease Grading 从单眼视频中绘制眼表并应用于干眼病分级

OMIA@MICCAI Pub Date : 2022-09-02 DOI: 10.1007/978-3-031-16525-2_7

Ikram Brahim, M. Lamard, A. Benyoussef, Pierre-Henri Conze, B. Cochener, D. Cornec, G. Quellec

引用次数: 0

Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images 基于特征表示学习的光学相干断层成像视网膜疾病鲁棒检测

OMIA@MICCAI Pub Date : 2022-06-24 DOI: 10.48550/arXiv.2206.12136

Sharif Amit Kamran, Khondker Fariha Hossain, A. Tavakkoli, S. Zuckerbrod, Salah A. Baker

{"title":"Feature Representation Learning for Robust Retinal Disease Detection from Optical Coherence Tomography Images","authors":"Sharif Amit Kamran, Khondker Fariha Hossain, A. Tavakkoli, S. Zuckerbrod, Salah A. Baker","doi":"10.48550/arXiv.2206.12136","DOIUrl":"https://doi.org/10.48550/arXiv.2206.12136","url":null,"abstract":". Ophthalmic images may contain identical-looking pathologies that can cause failure in automated techniques to distinguish different retinal degenerative diseases. Additionally, reliance on large annotated datasets and lack of knowledge distillation can restrict ML-based clinical support systems’ deploy-ment in real-world environments. To improve the robustness and transferability of knowledge, an enhanced feature-learning module is required to extract mean-ingful spatial representations from the retinal subspace. Such a module, if used effectively, can detect unique disease traits and differentiate the severity of such retinal degenerative pathologies. In this work, we propose a robust disease detection architecture with three learning heads, i) A supervised encoder for retinal disease classiﬁcation, ii) An unsupervised decoder for the reconstruction of disease-speciﬁc spatial information, and iii) A novel representation learning module for learning the similarity between encoder-decoder feature and enhancing the accuracy of the model. Our experimental results on two publicly available OCT datasets illustrate that the proposed model outperforms existing state-of-the-art models in terms of accuracy, interpretability, and robustness for out-of-distribution retinal disease detection.","PeriodicalId":261989,"journal":{"name":"OMIA@MICCAI","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124908303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

GARDNet: Robust Multi-view Network for Glaucoma Classification in Color Fundus Images garnet:用于青光眼彩色眼底图像分类的鲁棒多视点网络

OMIA@MICCAI Pub Date : 2022-05-25 DOI: 10.1007/978-3-031-16525-2_16

Ahmed Al Mahrooqi, Dmitrii Medvedev, Rand Muhtaseb, Mohammad Yaqub

引用次数: 1

The Unreasonable Effectiveness of Encoder-Decoder Networks for Retinal Vessel Segmentation 编码器-解码器网络在视网膜血管分割中的不合理效果

OMIA@MICCAI Pub Date : 2020-11-25 DOI: 10.1007/978-3-030-63419-3_5

Björn Browatzki, Jörn-Philipp Lies, C. Wallraven

引用次数: 0

Weakly Supervised Retinal Detachment Segmentation Using Deep Feature Propagation Learning in SD-OCT Images SD-OCT图像中基于深度特征传播学习的弱监督视网膜脱离分割

OMIA@MICCAI Pub Date : 2020-10-08 DOI: 10.1007/978-3-030-63419-3_15

Tieqiao Wang, Sijie Niu, Jiwen Dong, Yuehui Chen