Disclosed are a method and device for analyzing human tissue on the basis of a medical image. A tissue analysis device generates training data including a two-dimensional medical image and volume information of tissue by using a three-dimensional medical image, and trains, by using the training data, an artificial intelligence model that obtains a three-dimensional size, volume, or weight of tissue by dividing at least one or more normal or diseased tissues from a two-dimensional medical image in which a plurality of tissues are displayed overlapping on the same plane. In addition, the tissue analysis device obtains a three-dimensional size, volume, or weight of normal or diseased tissue from an X-ray medical image by using the artificial intelligence model.

A method includes, receiving: (i) a selected three-dimensional (3D) section that has been ablated in a patient organ in accordance with a specified contour, and (ii) a dataset, which is indicative of a set of lesions formed during ablation of the selected 3D section. The selected 3D section is transformed into a two-dimensional (2D) map, and checking, on the 2D map, whether the set of lesions covers the specified contour.

A method includes, receiving: (i) a selected three-dimensional (3D) section that has been ablated in a patient organ in accordance with a specified contour, and (ii) a dataset, which is indicative of a set of lesions formed during ablation of the selected 3D section. The selected 3D section is transformed into a two-dimensional (2D) map, and checking, on the 2D map, whether the set of lesions covers the specified contour.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

The present disclosure provides methods, systems, and non-transitory computer-readable media for assessment of disease treatment or progression on a lesion-by-lesion level. The systems and methods are based on measurements of a variety of features including total number of lesions, total number and proportion of lesions regressing or progressing, changes in dimensions of a lesion over time, and uptake values of a molecular imaging agent.

Systems and methods are disclosed for analyzing data from oncology treatments such as immune checkpoint inhibitor or radiotherapy therapies, including predicting adverse events of the oncology therapies, predicting objective response of the oncology therapies, predicting symptoms from the oncology therapies, and use of such predictions by technological implementations to achieve improved system and medical outcomes. An example technique for generating a predicted treatment outcome includes: receiving patient data for a human subject, which provides patient-reported outcomes collected from the human subject relating to a particular oncology treatment; processing the patient data with a trained artificial intelligence (AI) prediction model, which receives the patient data as input and produces a prediction of a treatment outcome as output; and outputting data to modify a treatment workflow of an oncology treatment for the human subject, based on the prediction of the treatment outcome.

Accurate tissue segmentation is performed without a priori knowledge of tissue type or other extrinsic information not found within the subject image, and may be combined with classification analysis so that diseased tissue is not only delineated within an image but also characterized in terms of disease type. In various embodiments, a source image is decomposed into smaller overlapping subimages such as square or rectangular tiles. A predictor such as a convolutional neural network produces tile-level classifications that are aggregated to produce a tissue segmentation and, in some embodiments, to classify the source image or a subregion thereof.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

Systems and methods for self-supervised representation learning as a means to generate context-specific pretrained models include selecting data from a set of available data sets; selecting a pretext task from domain specific pretext tasks; selecting a target problem specific network architecture based on a user selection from available choices or any customized model as per user preference; and generating a pretrained model for the selected network architecture using the selected data obtained from the set of available data sets and a pretext task as obtained from domain specific pretext tasks.

A method for parallel processing a digitally scanned pathology image is performed by a plurality of processors and includes performing, by a first processor, a first operation of generating a first batch from a first set of patches extracted from a digitally scanned pathology image and providing the generated first batch to a second processor, performing, by the first processor, a second operation of generating a second batch from a second set of patches extracted from the digitally scanned pathology image and providing the generated second batch to the second processor, and performing, by the second processor, a third operation of outputting a first analysis result from the first batch by using a machine learning model, with at least part of time frame for the second operation performed by the first processor overlapping at least part of time frame for the third operation performed by the second processor.