Abstract: Provided are a diagnostic imaging device, a diagnostic imaging method, a diagnostic imaging program and a learned model, which can improve the diagnostic accuracy for esophagus cancer in esophagogastroduodenoscopy. This diagnostic imaging device comprises: an endoscopic image acquisition unit for acquiring an endoscopic moving image showing the esophagus of a person being tested; an estimation unit for estimating the location of esophagus cancer that is present in the acquired endoscopic moving image, using a convolutional neural network that has learned from esophagus cancer images as training data, the esophagus cancer images having been obtained from esophaguses affected by esophagus cancer; and a display control unit that superimposes on the endoscopic moving image an estimated location of the esophagus cancer and a certainty factor serving as an index for the probability that esophagus cancer is present at that location.

Abstract: Provided are: an image diagnosis assistance apparatus capable of assisting diagnosis of an endoscopic image captured by an endoscopist; a data collection method; an image diagnosis assistance method; and an image diagnosis assistance program. The image diagnosis assistance apparatus is provided with: a lesion assessment unit that assesses, by a convolutional neural network, the denomination and the position of a lesion which is present in a digestive system endoscopic image of a patient captured by a digestive system endoscopic imaging device and information about accuracies thereof; and a display control unit that performs control for generating an analysis result image in which the denomination and the position of the lesion and the accuracies thereof are displayed and for displaying the image on the digestive system endoscopic image.

Abstract: Provided are: an image diagnosis assistance apparatus capable of assisting diagnosis of an endoscopic image captured by an endoscopist; a data collection method; an image diagnosis assistance method; and an image diagnosis assistance program. The image diagnosis assistance apparatus is provided with: a lesion assessment unit that assesses, by a convolutional neural network, the denomination and the position of a lesion which is present in a digestive system endoscopic image of a patient captured by a digestive system endoscopic imaging device and information about accuracies thereof; and a display control unit that performs control for generating an analysis result image in which the denomination and the position of the lesion and the accuracies thereof are displayed and for displaying the image on the digestive system endoscopic image.