Abstract: Methods and systems for medical diagnosis by machine learning are disclosed. Imaging data obtained from different medical techniques can be used as a training set for a machine learning method, to allow diagnosis of medical conditions in a faster a more efficient manner. A three-dimensional convolutional neural network can be employed to interpret volumetric data available from multiple scans of a patient.

Abstract: Methods and systems for medical diagnosis by machine learning are disclosed. Imaging data obtained from different medical techniques can be used as a training set for a machine learning method, to allow diagnosis of medical conditions in a faster a more efficient manner. A three-dimensional convolutional neural network can be employed to interpret volumetric data available from multiple scans of a patient. The imaging data can be analyzed according to a forced taxonomy and any discrepancy in the labels of the taxonomy during data analysis by machine learning and human experts can be resolved based on the forced taxonomy.

Abstract: Methods and systems for medical diagnosis by machine learning are disclosed. Imaging data obtained from different medical techniques can be used as a training set for a machine learning method, to allow diagnosis of medical conditions in a faster a more efficient manner. A three-dimensional convolutional neural network can be employed to interpret volumetric data available from multiple scans of a patient. The imaging data can be analyzed in real-time to prescribe additional testing while the patient is still within the medical facility.

Abstract: Methods and systems for transforming image data and training or testing neural networks. Images from textbooks can contain valuable related text. Transforming the related text into discrete determinate labels can be performed using natural language processing. Once transformed, the images and the labels can be advantageously used together to train neural networks.