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medical-5

Diabetic Retinopathy Detection

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Diabetic Retinopathy Debrecen Data Set

Libraries

Use these libraries to find diabetic-retinopathy-detection-5 models and implementations

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Diabetic Retinopathy Detection Dataset

Retina Benchmark

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Most implemented papers

Maximum Likelihood with Bias-Corrected Calibration is Hard-To-Beat at Label Shift Adaptation

Anshul B Kundaje, Avanti Shrikumar, Amr M. Alexandari•Tue Dec 31 2019

It is shown that combining maximum likelihood with a type of calibration the authors call bias-corrected calibration outperforms both BBSL and RLLS across diverse datasets and distribution shifts, and it is proved that the maximum likelihood objective is concave.

117

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Diabetic Retinopathy Detection via Deep Convolutional Networks for Discriminative Localization and Visual Explanation

Zhiguang Wang, Jianbo Yang•Thu Mar 30 2017

The proposed CNN model can achieve high performance on DR detection compared with the state-of-the-art while achieving the merits of providing the RAM to highlight the salient regions of the input image.

117 0

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Transfer Learning based Detection of Diabetic Retinopathy from Small Dataset

Misgina Tsighe Hagos, Shri Kant•Thu May 16 2019

This work sub-sampled a smaller version of the Kaggle Diabetic Retinopathy classification challenge dataset for model training, and tested the model's accuracy on a previously unseen data subset, which could be used in other deep learning based medical image classification problems facing the challenge of labeled training data insufficiency.

101 0

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Deep Learning Approach to Diabetic Retinopathy Detection

B. Tymchenko, Philip Marchenko, D. Spodarets•Mon Mar 02 2020

An automatic deep-learning-based method for stage detection of diabetic retinopathy by single photography of the human fundus and the multistage approach to transfer learning, which makes use of similar datasets with different labeling are proposed.

181 0

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3D Self-Supervised Methods for Medical Imaging

Aiham Taleb, W. Loetzsch, Noel Danz, Julius Severin, Thomas Gaertner, Benjamin Bergner, Christoph Lippert•Fri Jun 05 2020

This work proposes 3D versions for five different self-supervised methods, in the form of proxy tasks, to facilitate neural network feature learning from unlabeled 3D images, aiming to reduce the required cost for expert annotation.

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A unified technique for entropy enhancement based diabetic retinopathy detection using hybrid neural network

Fatima, Muhammad Imran, A. Ullah, Muhammad Arif, R. Noor•Mon Feb 28 2022

A novel entropy enhancement technique is devised exploiting the discrete wavelet transforms to improve the visibility of the medical images by making the subtle features more prominent and the comparison of the proposed scheme with some of the best contemporary schemes shows the significant improvement of the techniques in terms of diabetic retinopathy classification.

37 0

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Curvature-based Feature Selection with Application in Classifying Electronic Health Records

Jie Li, N. A. Moubayed, Z. Zuo•Sat Jan 09 2021

An efficient filter-based feature selection method, namely Curvature-based Feature Selection (CFS), is presented that achieved state-of-the-art performance on the above data sets against conventional PCA and other most recent approaches.

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Explainable Diabetic Retinopathy Detection and Retinal Image Generation

Yitian Zhao, Feng Lu, Yuhao Niu, Lin Gu•Wed Jun 30 2021

By determining and isolating the neuron activation patterns on which diabetic retinopathy (DR) detector relies to make decisions, it is demonstrated the direct relation between the isolated neuron activation and lesions for a pathological explanation.

68 0

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Diabetic Retinopathy Detection | State-of-the-Art