Directional wave measurements during the Hr. Ms. Tydeman sea trial / by Robert J. Bachman and Edward W. Foley

Bachman, Robert J.; Foley, Edward

doi:10.5962/bhl.title.46789

Cited by 3 publications

(11 citation statements)

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“…Examples of networks using such strategies include Deep InfoMax and, specifically, Augmented Multiscale Deep InfoMax (AMDIM) used here, which is trained by maximizing the similarity of a pair of local and global image representations, taken from different layers of the network, that were created from augmentations of the same source image, and by minimizing the similarity of pairs not created from the same source image (contrastive learning). Consequently, each image should be given as unique a representation as possible, while maintaining that the local and global representations are still heavily correlated under certain transformations.…”

Section: Methodsmentioning

confidence: 99%

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases

et al. 2020

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IMPORTANCERecent studies have demonstrated the successful application of artificial intelligence (AI) for automated retinal disease diagnostics but have not addressed a fundamental challenge for deep learning systems: the current need for large, criterion standard-annotated retinal data sets for training. Low-shot learning algorithms, aiming to learn from a relatively low number of training data, may be beneficial for clinical situations involving rare retinal diseases or when addressing potential bias resulting from data that may not adequately represent certain groups for training, such as individuals older than 85 years.OBJECTIVE To evaluate whether low-shot deep learning methods are beneficial when using small training data sets for automated retinal diagnostics. DESIGN, SETTING, AND PARTICIPANTSThis cross-sectional study, conducted from July 1, 2019, to June 21, 2020, compared different diabetic retinopathy classification algorithms, traditional and low-shot, for 2-class designations (diabetic retinopathy warranting referral vs not warranting referral). The public domain EyePACS data set was used, which originally included 88 692 fundi from 44 346 individuals. Statistical analysis was performed from February 1 to June 21, 2020. MAIN OUTCOMES AND MEASURESThe performance (95% CIs) of the various AI algorithms was measured via receiver operating curves and their area under the curve (AUC), precision recall curves, accuracy, and F1 score, evaluated for different training data sizes, ranging from 5120 to 10 samples per class. RESULTSDeep learning algorithms, when trained with sufficiently large data sets (5120 samples per class), yielded comparable performance, with an AUC of 0.8330 (95% CI, 0.8140-0.8520) for a traditional approach (eg, fined-tuned ResNet), compared with low-shot methods (AUC, 0.8348 [95% CI, 0.8159-0.8537]) (using self-supervised Deep InfoMax [our method denoted as DIM]). However, when far fewer training images were available (n = 160), the traditional deep learning approach had an AUC decreasing to 0.6585 (95% CI, 0.6332-0.6838) and was outperformed by a low-shot method using self-supervision with an AUC of 0.7467 (95% CI, 0.7239-0.7695). At very low shots (n = 10), the traditional approach had performance close to chance, with an AUC of 0.5178 (95% CI, 0.4909-0.5447) compared with the best low-shot method (AUC, 0.5778 [95% CI, 0.5512-0.6044]).CONCLUSIONS AND RELEVANCE These findings suggest the potential benefits of using low-shot methods for AI retinal diagnostics when a limited number of annotated training retinal images are available (eg, with rare ophthalmic diseases or when addressing potential AI bias).

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Section: Methodsmentioning

confidence: 99%

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases

et al. 2020

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“…Finally, AI research has recently tackled unsupervised problems where training data have no labels via self-supervised techniques, eg, networks trained to predict one image patch from another patch within the same image. Embeddings obtained via a pretrained self-supervised networks were used in one of our anomaly detection methods.…”

Section: Methodsmentioning

confidence: 99%

“…Discriminative embedding systems used either a fully supervised network (InceptionV3) or a self-supervised network (Deep InfoMax). Both networks were pretrained on ImageNet.…”

Section: Methodsmentioning

confidence: 99%

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Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning

et al. 2022

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IMPORTANCE Anomaly detectors could be pursued for retinal diagnoses based on artificial intelligence systems that may not have access to training examples for all retinal diseases in all phenotypic presentations. Possible applications could include screening of population for any retinal disease rather than a specific disease such as diabetic retinopathy, detection of novel retinal diseases or novel presentations of common retinal diseases, and detection of rare diseases with little or no data available for training.OBJECTIVE To study the application of anomaly detection to retinal diseases.DESIGN, SETTING, AND PARTICIPANTS High-resolution retinal images from the publicly available EyePACS data set with fundus images with a corresponding label ranging from 0 to 4 for representing different severities of diabetic retinopathy. Sixteen variants of anomaly detectors were designed. For evaluation, a surrogate problem was constructed, using diabetic retinopathy images, in which only retinas with nonreferable diabetic retinopathy, ie, no diabetic macular edema, and no diabetic retinopathy or mild to moderate nonproliferative diabetic retinopathy were used for training an artificial intelligence system, but both nonreferable and referable diabetic retinopathy (including diabetic macular edema or proliferative diabetic retinopathy) were used to test the system for detecting retinal disease. MAIN OUTCOMES AND MEASURESAnomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy.RESULTS A total of 88 692 high-resolution retinal images of 44 346 individuals with varying severity of diabetic retinopathy were analyzed. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.808 (95% CI, 0.789-0.827) and was obtained using an embedding method that involved a self-supervised network.CONCLUSIONS AND RELEVANCE This study suggests when abnormal (diseased) data, ie, referable diabetic retinopathy in this study, were not available for training of retinal diagnostic systems wherein only nonreferable diabetic retinopathy was used for training, anomaly detection techniques were useful in identifying images with and without referable diabetic retinopathy. This suggests that anomaly detectors may be used to detect retinal diseases in more generalized settings and potentially could play a role in screening of populations for retinal diseases or identifying novel diseases and phenotyping or detecting unusual presentations of common retinal diseases.

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“…Photolysis may be used to convert a substance into more readily detected products, for example, the introduction of fluorescence or electrochemical activity [552,641,649,657,[659][660][661][662][663]. Photolysis may be used to convert a substance into more readily detected products, for example, the introduction of fluorescence or electrochemical activity [552,641,649,657,[659][660][661][662][663].…”

Section: Reaction Detectorsmentioning

confidence: 99%

Sample Preparation for Chromatographic Analysis

Poole

1991

Chromatography Today

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Directional wave measurements during the Hr. Ms. Tydeman sea trial / by Robert J. Bachman and Edward W. Foley

Cited by 3 publications

References 0 publications

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases

Low-Shot Deep Learning of Diabetic Retinopathy With Potential Applications to Address Artificial Intelligence Bias in Retinal Diagnostics and Rare Ophthalmic Diseases

Detecting Anomalies in Retinal Diseases Using Generative, Discriminative, and Self-supervised Deep Learning

Sample Preparation for Chromatographic Analysis

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