Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy
Detection
- URL: http://arxiv.org/abs/2005.14308v1
- Date: Thu, 28 May 2020 21:37:36 GMT
- Title: Combining Fine- and Coarse-Grained Classifiers for Diabetic Retinopathy
Detection
- Authors: Muhammad Naseer Bajwa, Yoshinobu Taniguchi, Muhammad Imran Malik,
Wolfgang Neumeier, Andreas Dengel, Sheraz Ahmed
- Abstract summary: Visual artefacts of early diabetic retinopathy in retinal fundus images are usually small in size, inconspicuous, and scattered all over retina.
We propose to combine coarse-grained classifiers that detect discriminating features from the whole images, with a recent breed of fine-grained classifiers that discover and pay particular attention to pathologically significant regions.
- Score: 6.201033439090515
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Visual artefacts of early diabetic retinopathy in retinal fundus images are
usually small in size, inconspicuous, and scattered all over retina. Detecting
diabetic retinopathy requires physicians to look at the whole image and fixate
on some specific regions to locate potential biomarkers of the disease.
Therefore, getting inspiration from ophthalmologist, we propose to combine
coarse-grained classifiers that detect discriminating features from the whole
images, with a recent breed of fine-grained classifiers that discover and pay
particular attention to pathologically significant regions. To evaluate the
performance of this proposed ensemble, we used publicly available EyePACS and
Messidor datasets. Extensive experimentation for binary, ternary and quaternary
classification shows that this ensemble largely outperforms individual image
classifiers as well as most of the published works in most training setups for
diabetic retinopathy detection. Furthermore, the performance of fine-grained
classifiers is found notably superior than coarse-grained image classifiers
encouraging the development of task-oriented fine-grained classifiers modelled
after specialist ophthalmologists.
Related papers
- Improving Classification of Retinal Fundus Image Using Flow Dynamics
Optimized Deep Learning Methods [0.0]
Diabetic Retinopathy (DR) refers to a barrier that takes place in diabetes mellitus damaging the blood vessel network present in the retina.
It can take some time to perform a DR diagnosis using color fundus pictures because experienced clinicians are required to identify the tumors in the imagery used to identify the illness.
arXiv Detail & Related papers (2023-04-29T16:11:34Z) - GraVIS: Grouping Augmented Views from Independent Sources for
Dermatology Analysis [52.04899592688968]
We propose GraVIS, which is specifically optimized for learning self-supervised features from dermatology images.
GraVIS significantly outperforms its transfer learning and self-supervised learning counterparts in both lesion segmentation and disease classification tasks.
arXiv Detail & Related papers (2023-01-11T11:38:37Z) - Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for
Ophthalmic Images [18.186766129476077]
We propose an artifact-tolerant unsupervised learning framework termed EyeLearn for learning representations of ophthalmic images.
EyeLearn has an artifact correction module to learn representations that can best predict artifact-free ophthalmic images.
To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection using a real-world ophthalmic image dataset of glaucoma patients.
arXiv Detail & Related papers (2022-09-02T01:25:45Z) - Deep Semi-Supervised and Self-Supervised Learning for Diabetic
Retinopathy Detection [0.0]
Diabetic retinopathy is one of the leading causes of blindness in the working-age population of developed countries.
Deep neural networks have been widely used in automated systems for DR classification on eye fundus images.
This paper presents a semi-supervised method that leverages unlabeled images and labeled ones to train a model that detects diabetic retinopathy.
arXiv Detail & Related papers (2022-08-04T02:28:13Z) - MTCD: Cataract Detection via Near Infrared Eye Images [69.62768493464053]
cataract is a common eye disease and one of the leading causes of blindness and vision impairment.
We present a novel algorithm for cataract detection using near-infrared eye images.
Deep learning-based eye segmentation and multitask network classification networks are presented.
arXiv Detail & Related papers (2021-10-06T08:10:28Z) - Self-Supervised Learning from Unlabeled Fundus Photographs Improves
Segmentation of the Retina [4.815051667870375]
Fundus photography is the primary method for retinal imaging and essential for diabetic retinopathy prevention.
Current segmentation methods are not robust towards the diversity in imaging conditions and pathologies typical for real-world clinical applications.
We utilize contrastive self-supervised learning to exploit the large variety of unlabeled fundus images in the publicly available EyePACS dataset.
arXiv Detail & Related papers (2021-08-05T18:02:56Z) - An Interpretable Multiple-Instance Approach for the Detection of
referable Diabetic Retinopathy from Fundus Images [72.94446225783697]
We propose a machine learning system for the detection of referable Diabetic Retinopathy in fundus images.
By extracting local information from image patches and combining it efficiently through an attention mechanism, our system is able to achieve high classification accuracy.
We evaluate our approach on publicly available retinal image datasets, in which it exhibits near state-of-the-art performance.
arXiv Detail & Related papers (2021-03-02T13:14:15Z) - Towards the Localisation of Lesions in Diabetic Retinopathy [2.3204178451683264]
This study uses pre-trained weights from four state-of-the-art deep learning models to produce and compare localisation maps of diabetic retinopathy (DR) fundus images.
InceptionV3 achieves the best performance with a test classification accuracy of 96.07%, and localise lesions better and faster than the other models.
arXiv Detail & Related papers (2020-12-21T15:39:17Z) - Few-shot Medical Image Segmentation using a Global Correlation Network
with Discriminative Embedding [60.89561661441736]
We propose a novel method for few-shot medical image segmentation.
We construct our few-shot image segmentor using a deep convolutional network trained episodically.
We enhance discriminability of deep embedding to encourage clustering of the feature domains of the same class.
arXiv Detail & Related papers (2020-12-10T04:01:07Z) - Modeling and Enhancing Low-quality Retinal Fundus Images [167.02325845822276]
Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis.
We propose a clinically oriented fundus enhancement network (cofe-Net) to suppress global degradation factors.
Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details.
arXiv Detail & Related papers (2020-05-12T08:01:16Z) - Weakly supervised multiple instance learning histopathological tumor
segmentation [51.085268272912415]
We propose a weakly supervised framework for whole slide imaging segmentation.
We exploit a multiple instance learning scheme for training models.
The proposed framework has been evaluated on multi-locations and multi-centric public data from The Cancer Genome Atlas and the PatchCamelyon dataset.
arXiv Detail & Related papers (2020-04-10T13:12:47Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.