Denka Company Limited

Niigata University

Shujiro Okuda

Hiroshi Izutsu

Keisuke Kodama

Toshifumi Wakai

Yoshifumi Shimada

Yu Watanabe

There is provided a data generation apparatus including a display control unit that displays, on a screen, an image and borders of a plurality of partial images generated by dividing the image into a plurality of pieces while the image and the borders are superimposed together, an input unit that receives input of a label to be given to each of the plurality of partial images, and a generation unit that generates learning data for causing a learning model to learn by associating each of the plurality of partial images with the label given to the corresponding partial image of the plurality of partial images.

Data generation apparatus, data generation method, and program

A method, a program, and a method determining hypermutated type cancer with higher accuracy than before is provided. Provided is a system for determining hypermutated cancer comprising, an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data and a plurality of third image data, wherein the first image data represents an image of a pathological section of stained hypermutated cancer, the second image data represents an image of a pathological section of cancer which is not hypermutated, and is stained same as the pathological section of the first image data, and the third image data represents an image of a pathological section of cancer which is newly determined whether hypermutated or not, and is stained same as the pathological section of the first image data, a holding unit configured to be capable of holding a first image data and a second image data, a machine learning execution unit configured to be capable of generating a determination model determining whether a cancer is hypermutated or not, using the first image data and the second image data held by the holding unit as training data, and a determining unit configured to be capable of determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the determination model.

System, program, and method for determining hypermutated tumor

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Niigata, Japan

Shujiro Okuda