Unitedhealth Group Incorporated

University of California

Doheny Eye Institute

Eran Halperin

Joel David Stremmel

Ardavan Saeedi

Brian Hill

Nadav Rakocz

Muneeswar Gupta

Sanjit S Batra

Hamid Reza Hassanzadeh

Jeffrey Chiang

Deep learning methods and systems for detecting biomarkers within optical coherence tomography volumes using such deep learning methods and systems are provided. Embodiments predict the presence or absence of clinically useful biomarkers in OCT images using deep neural networks. The lack of available training data for canonical deep learning approaches is overcome in embodiments by leveraging a large external dataset consisting of foveal scans using transfer learning. Embodiments represent the three-dimensional OCT volume by 'tiling' each slice into a single two dimensional image, and adding an additional component to encourage the network to consider local spatial structure. Methods and systems, according to embodiments are able to identify the presence or absence of AMD-related biomarkers on par with clinicians. Beyond identifying biomarkers, additional models could be trained, according to embodiments, to predict the progression of these biomarkers over time.

Retinal disease biomarker prediction by stacking slices of 3D OCT image to reshape into 2D image and applying trained feature extractor and CNN

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for improving question-answer (QA) machine learning model training based on generating predicted label indicators, generating prediction score indicators and prediction explanation indicators, generating structured label-explanation datasets, generating synthetic QA training datasets, generating a prediction output, and initiating the performance of one or more prediction-based operations based on the prediction output.

Generation of synthetic question-answer pairs using a document classifier and classification explainer

Various embodiments of the present disclosure provide methods, apparatus, systems, computing devices, computing entities, and/or the like for processing document classification system outputs, wherein classification routine iterations are performed using masked document data objects comprising one or more masked text blocks. Text block importance score for text blocks are generated and compared to generate predictive data output comprising text blocks determined to be the most influential in classifying the document data objects with respect to one or more classification labels.

Systems and methods for processing machine learning language model classification outputs via text block masking

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Eran Halperin