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, a plurality of third image data, and non-cancer 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; and the non-cancer image data represents an image which is not a pathological section of cancer; a holding unit configured to be capable of holding the first image data and the second image data; and the non-cancer image data; a machine learning execution unit configured to be capable of generating a first determination model determining whether an image represents a pathological section of cancer or not, using the first image data, the second image data and the non-cancer image data held by the holding unit as training data, and a second 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 cancer or not, by inputting the third image data into the first determination model, and when the third image data is determined as an image of cancer, determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the second determination model. an input unit configured to be capable of inputting a plurality of first image data, a plurality of second image data, a plurality of third image data, and non-cancer 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; and the non-cancer image data represents an image which is not a pathological section of cancer; 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; and the non-cancer image data represents an image which is not a pathological section of cancer; a holding unit configured to be capable of holding the first image data and the second image data; and the non-cancer image data; a machine learning execution unit configured to be capable of generating a first determination model determining whether an image represents a pathological section of cancer or not, using the first image data, the second image data and the non-cancer image data held by the holding unit as training data, and a second 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 first determination model determining whether an image represents a pathological section of cancer or not, using the first image data, the second image data and the non-cancer image data held by the holding unit as training data, and a second 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 cancer or not, by inputting the third image data into the first determination model, and when the third image data is determined as an image of cancer, determining whether the third image data represents an image of hypermutated cancer or not, by inputting the third image data into the second determination model.