Abstract: Disclosed herein are a method and system for processing medical image data. The method can comprise querying, using one or more monitor processors of a Picture Archiving and Communication System (PACS) monitor, a storage unit on a PACS server for available image data; determining, using the one or more monitor processors, if the available image data is new image data; retrieving, using the one or more monitor processors, the new image data from the storage unit on the PACS server if the available image data is new image data; processing, using one or more model processors, the new image data using a machine learning model to obtain a model result; generating, using the one or more model processors, at least one of an enhanced image data and a model result report based on the model result; and storing the at least one of the enhanced image data and the model result report for retrieval by a computing device.

Abstract: Disclosed are methods and systems for processing medical image data. The method comprising inputting, with one or more processors of one or more computation devices, medical image data into a model for nodule detection; calculating, for at least one nodule detected by the model for nodule detection, a nodule histogram of all voxel intensities of said nodule; determining, from each nodule histogram, a nodule classification among a plurality of nodule classifications for the at least one nodule.

Abstract: Disclosed are methods and systems for processing medical image data. The method comprising inputting, with one or more processors of one or more computation devices, medical image data into an encoder stage of an encoder-decoder pair (EDP) as a first input among one or more inputs; calculating, with the one or more processors, a latent space representation of the one or more inputs using the encoder stage of the EDP; providing, from a latent space database stored within one or more storage devices accessible by the one or more computation devices, latent space representations of other inputs; and determining, with the one or more processors, a classification based on the latent space representation of the one or more inputs and at least one latent space representation of the other inputs.

Abstract: The invention provides a method and device for creating a model for classifying a data point in imaging data representing measured intensities, the method comprising: training a model using a first labelled set of imaging data points; determining at least one first image part in the first labelled set which the model incorrectly classifies; generating second image parts similar to at least one image part; further training the model using the second image parts. Preferably the imaging data points and the second image parts comprise 3D data points.

Abstract: The invention provides a method and apparatus for classifying a region of interest in imaging data, the method comprising: calculating a feature vector for at least one region of interest in the imaging data; projecting the feature vector for the at least one region of interest in the imaging data using a plurality of decision functions to generate a corresponding plurality of classifications; calculating an ensemble classification based on the plurality of classifications. receiving from the user feedback information concerning the ensemble classification; forming an additional classified feature vector from the feature vector and the feedback information; and updating at least one of the plurality of decision functions using the additional classified feature vector.

Abstract: The invention provides a method and device for creating a model for classifying a data point in imaging data representing measured intensities, the method comprising: training a model using a first labelled set of imaging data points; determining at least one first image part in the first labelled set which the model incorrectly classifies; generating second image parts similar to at least one image part; further training the model using the second image parts. Preferably the imaging data points and the second image parts comprise 3D data points.

Abstract: The invention provides a method and apparatus for classifying a region of interest in imaging data, the method comprising: calculating a feature vector for at least one region of interest in the imaging data, said feature vector including features of a first modality; projecting the feature vector for the at least one region of interest in the imaging data using a decision function to generate a classification, wherein the decision function is based on classified feature vectors including features of a first modality and features of a second modality; estimating the confidence of the classification if the feature vector is enhanced with features of the second modality.

Abstract: The invention provides a method and apparatus for classifying a region of interest in imaging data, the method comprising: calculating a feature vector for at least one region of interest in the imaging data; projecting the feature vector for the at least one region of interest in the imaging data using a plurality of decision functions to generate a corresponding plurality of classifications; calculating an ensemble classification based on the plurality of classifications. receiving from the user feedback information concerning the ensemble classification; forming an additional classified feature vector from the feature vector and the feedback information; and updating at least one of the plurality of decision functions using the additional classified feature vector.

Abstract: The invention provides a method and apparatus for classifying a region of interest in imaging data, the method comprising: calculating a feature vector for at least one region of interest in the imaging data, said feature vector including features of a first modality; projecting the feature vector for the at least one region of interest in the imaging data using a decision function to generate a classification, wherein the decision function is based on classified feature vectors including features of a first modality and features of a second modality; estimating the confidence of the classification if the feature vector is enhanced with features of the second modality.