Evaluating Transfer Learning for Macular Fluid Detection with Limited Data

Cazañas-Gordón, Alex; Parra-Mora, Esther; Cruz, Luís A. da Silva

doi:10.23919/eusipco47968.2020.9287859

Cited by 8 publications

(4 citation statements)

References 13 publications

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“…The larger the mini-batch size the lower the performance, and vice versa. This observation is consistent with prior work regarding the optimization of this hyperparameter [39], [53], and suggests that setting the batch size to the largest value that fits in memory might constraint the model to settle at a sub-optimal solution. On the other hand, we also verified that the mini-batch size is directly related to the training duration, with smaller mini-batches leading to longer training times.…”

Section: Resultssupporting

confidence: 85%

“…Image classification, segmentation, and detection are the most common tasks when analyzing medical images, with the most common application being deciding about the presence or absence of a specific disease. In the context of eye diseases expressed as retinal anomalies, researchers have demonstrated the efficacy of deep learning for the detection of macular edema [39], diabetic retinopathy [44], retinal detachments [45], retinopathy of prematurity [46], among others. These computer-aided systems help health care professionals diagnose and decide starting treatment of these diseases in a timely and efficient manner.…”

Section: E State Of the Art On Epiretinal Membrane Automatic Detection Methodsmentioning

confidence: 99%

“…During this limited retraining process, the model is adapted to the characteristics of the dataset of interest [35]. Several research reports describe the successful application of transfer learning to solve medical diagnosis problems involving automated image analysis, with the most common reason for using transfer learning being the limited access to labeled data [36]- [39]. The advantages of this approach are also reported in some of the works reviewed by Zou et al [40].…”

Section: Transfer Learningmentioning

confidence: 99%

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Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

et al. 2021

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Epiretinal membrane (ERM) is an eye disease that affects 7% of the world population, with a higher incidence in people over 75 years old. If left untreated, it can lead to complications in the central vision, resulting in severe vision loss. Early detection is important for progress follow-up, treatment monitoring, and to avoid total vision loss. Optical coherence tomography, a non-invasive retina imaging technique, can be used for effective detection and monitoring of this condition. To date, automatic methods to detect ERM have received little attention in the research literature. This article describes the application of deep learning to the automatic detection of ERM. The proposed solution is based on four widely used convolutional neural network architectures adapted to the task using transfer learning, and fine-tuned with a proprietary dataset. The architectures were specialized by optimizing the network hyperparameters and two loss functions, cross-entropy and focal loss. A detailed description of the methods is provided, complemented with an exhaustive evaluation of their performance. Overall, the methods reached an accuracy of 99.7%, with sensitivity and specificity of 99.47% and 99.93%, respectively. The results showed that transfer learning enabled a successful use of deep learning to detect ERM in optical coherence tomography retinal images, even when only relatively small training datasets are available.INDEX TERMS Artificial intelligence, deep learning, epiretinal membrane, macular puker, neural networks, optical coherence tomography, transfer learning.

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Section: Resultssupporting

confidence: 85%

Section: E State Of the Art On Epiretinal Membrane Automatic Detection Methodsmentioning

confidence: 99%

Section: Transfer Learningmentioning

confidence: 99%

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Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

et al. 2021

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“…Large minibatch sizes resulted in shorter training but higher accuracy, whereas short minibatch sizes resulted in the opposite. This observation is consistent with prior research regarding this hyper-parameter optimization [60], [69]. To inform the selection of the base architecture, we observe the performance and training time.…”

Section: Discussionsupporting

confidence: 84%

Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

2021

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Manual assessment of the retinal thickness in optical coherence tomography images is a timeconsuming task, prone to error and inter-observer variability. The wide variability of the retinal appearance makes the automation of retinal image processing a challenging problem to tackle. The difficulty is even more accentuated in practice when the retinal tissue exhibits large structural changes due to disruptive pathology. In this work, we propose an ensemble-learning-based method for the automated segmentation of retinal boundaries in optical coherence tomography images that is robust to retinal abnormalities. The segmentation accuracy of the proposed algorithm was evaluated on two publicly available datasets that included cases of severe retinal edema. Moreover, the performance of the proposed method was compared to two existing methods, widely referenced in the relevant literature. The proposed algorithm outperformed reference methods at segmenting the retinal boundaries in both normal and pathological images. Furthermore, a thorough reliability analysis showed a strong agreement between the retinal thickness measurements derived from the segmentation obtained with the proposed method and corresponding manual measurements computed with the manual annotations. INDEX TERMSDeep learning, ensemble learning, optical coherence tomography (OCT), retinal thickness VOLUME x, 202x

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Artificial intelligence for visually impaired

Wang

Zhang

2023

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Evaluating Transfer Learning for Macular Fluid Detection with Limited Data

Cited by 8 publications

References 13 publications

Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

Epiretinal Membrane Detection in Optical Coherence Tomography Retinal Images Using Deep Learning

Ensemble Learning Approach to Retinal Thickness Assessment in Optical Coherence Tomography

Artificial intelligence for visually impaired

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