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 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 a first pathological slide image one of a first object and biological information of the first object, generate training data by using at least one first patch included in the first pathological slide image, and the biological information, train a first machine learning model based on the training data, and analyze a second pathological slide image of a second object by using the trained first machine learning model.

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: Provided is a computing apparatus including: at least one memory; and at least one processor, wherein the at least one processor is configured to: perform a first classification on a plurality of tissues expressed in a pathological slide image by analyzing the pathological slide image, perform a second classification on a plurality of cells expressed in a pathological slide image by analyzing the pathological slide image, and calculate tumor purity including information on noise included in the pathological slide image by combining a first classification result and a second classification result.

Abstract: Provided is a computing apparatus including: at least one memory; and at least one processor, wherein the at least one processor is configured to: perform a first classification on a plurality of tissues expressed in a pathological slide image by analyzing the pathological slide image, perform a second classification on a plurality of cells expressed in a pathological slide image by analyzing the pathological slide image, and calculate tumor purity including information on noise included in the pathological slide image by combining a first classification result and a second classification result.

Abstract: The present invention relates to methods for treating colorectal cancer patients comprising selecting patients who express high level of the TGF-? response signatures and co-administering TGF-? inhibitor with human immunoglobulin. The invention also relates to the method of diagnosing and selecting patients which is likely to benefit from the treatment comprising the determination of the gene expression levels of TGF-? response signature by analyzing histopathologic images of stroma or RNA sequence from the patients.