Abstract: A computing apparatus includes: at least one memory; and at least one processor, wherein the processor generates quantitative information regarding at least one cell included in a region of interest of a pathological slide image by analyzing the pathological slide image, generates qualitative information regarding at least one tissue included in the pathological slide image by analyzing the pathological slide image, and controls a display apparatus to output at least one of the quantitative information and the qualitative information on the pathological slide image according to a manipulation of a user.

Abstract: Provided is a method for providing annotation information for a 3D image, which may include outputting a representative image for the 3D image including a plurality of slices, selecting at least one pixel associated with a target item from among a plurality of pixels included in the representative image, outputting, among the plurality of slices, a slice associated with the selected at least one pixel, and receiving an annotation for a partial region of the output slice.

Abstract: The present disclosure relates to a method, performed by at least one processor of an information processing system, of analyzing a pathological image. The method includes receiving a pathological image, detecting an object associated with medical information, in the received pathological image by using a machine learning model, generating an analysis result on the received pathological image, based on a result of the detecting, and outputting medical information about at least one region included in the pathological image, based on the analysis result.

Abstract: A method for generating a medical prediction related to a biomarker from medical data is provided, which includes obtaining medical data associated with a patient, determining a region of interest in the medical data, extracting one or more features associated with the medical data based on the region of interest, and generating a medical prediction for the patient based on the extracted one or more features.

Abstract: Provided is a method for analysing a pathology image, which is performed by at least one processor and includes acquiring a pathology image, inputting the acquired pathology image into a machine learning model and acquiring an analysis result for the pathology image from the machine learning model, and outputting the acquired analysis result, in which the machine learning model is a model trained by using a training data set generated based on a first pathology data set associated with a first domain and a second pathology data set associated with a second domain different from the first domain.

Abstract: Provided is a method for analysing a pathology image, which is performed by at least one processor and includes acquiring a pathology image, inputting the acquired pathology image into a machine learning model and acquiring an analysis result for the pathology image from the machine learning model, and outputting the acquired analysis result, in which the machine learning model is a model trained by using a training data set generated based on a first pathology data set associated with a first domain and a second pathology data set associated with a second domain different from the first domain.

Abstract: A computing device includes: at least one memory; and at least one processor, wherein the at least one processor is configured to obtain information related to tissues or cells represented in a pathological slide image by analyzing the pathological slide image, predict a ratio of circulating tumor deoxyribonucleic acid (DNA) to cell free DNA, based on the information, and generate guidance related to a follow-up examination, based on the ratio.

Abstract: 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.

Abstract: A computing device includes at least one memory and at least one processor. The at least one processor is configured to detect a plurality of tumor cells included in one or more tumor areas (cancer areas) from a pathological slide image, determine a cell expression class of the plurality of tumor cells, based on a biomarker expression degree of the plurality of tumor cells, and generate a heatmap image for the pathological slide image, based on a result of the determining.

Abstract: A computing device obtains information about a medical slide image, and determines a dataset type of the medical slide image and a panel of the medical slide image. The computing device assigns to an annotator account, an annotation job defined by at least the medical slide image, the determined dataset type, an annotation task, and a patch that is a partial area of the medical slide image. The annotation task includes the determined panel, and the panel is designated as one of a plurality of panels including a cell panel, a tissue panel, and a structure panel. The dataset type indicates a use of the medical slide image and is designated as one of a plurality of uses including a training use of a medical learning model and a validation use of the machine learning model.

Abstract: A method for interpreting an input image by a computing device operated by at least one processor is provided. The method for interpreting an input image comprises storing an artificial intelligent (AI) model that is trained to classify a lesion detected in the input image as suspicious or non-suspicious and, under a condition of being suspicious, to classify the lesion detected in the input image as malignant or benign-hard representing that the lesion is suspicious but determined to be benign, receiving an analysis target image, by using the AI model, obtaining a classification class of a target lesion detected in the analysis target image and, when the classification class is the suspicious, obtaining at least one of a probability of being suspicious, a probability of being benign-hard, and a probability of malignant for the target lesion, and outputting an interpretation result including at least one probability obtained for the target lesion.

Abstract: The present disclosure relates to a method, performed by at least one processor of an information processing system, of analyzing a pathological image. The method includes receiving a pathological image, detecting an object associated with medical information, in the received pathological image by using a machine learning model, generating an analysis result on the received pathological image, based on a result of the detecting, and outputting medical information about at least one region included in the pathological image, based on the analysis result.

Abstract: The present disclosure relates to a method for predicting biomarker expression from a medical image. The method for predicting biomarker expression includes receiving a medical image, and outputting indices of biomarker expression for the at least one lesion included in the medical image by using a first machine learning model.

Abstract: The present disclosure relates to a medical image analysis method using a processor and a memory which are hardware. The method includes generating predicted second metadata for a medical image by using a prediction model, and determining a processing method of the medical image based on one of first metadata stored corresponding to the medical image and the second metadata.

Abstract: The present disclosure relates to a method for predicting biomarker expression from a medical image. The method for predicting biomarker expression includes receiving a medical image, and outputting indices of biomarker expression for the at least one lesion included in the medical image by using a first machine learning model.

Abstract: A computing device includes at least one memory, and at least one processor configured to analyze at least one object expressed in a pathological slide image, evaluate quality of the pathological slide image based on a result of the analyzing, and perform at least one additional operation according to a result of the evaluating.

Abstract: Provided is a computing device including at least one memory, and at least one processor configured to obtain feature information corresponding to a pathological slide image, generate medical information associated with the pathological slide image based on the feature information, and output at least one of the medical information and additional information based on the medical information.

Abstract: Provided are a method and an apparatus for interlocking a lesion location between a 2D medical image and 3D tomosynthesis images including a plurality of 3D image slices.

Abstract: A computing device includes at least one memory, and at least one processor configured to generate, based on first analysis on a pathological slide image, first biomarker expression information, generate, based on a user input for updating at least some of results of the first analysis, second biomarker expression information about the pathological slide image, and control a display device to output a report including medical information about at least some regions included in the pathological slide image, based on at least one of the first biomarker expression information or the second biomarker expression information.

Abstract: A computing apparatus includes at least one memory, and at least one processor, wherein the processor is configured to acquire a pathological slide image showing at least one tissue, generate feature information related to at least one area of the pathological slide image, and detect, from the pathological slide image, at least one cell included in the at least one tissue by using the pathological slide image and the feature information.