MODEL GENERATION METHOD, MODEL GENERATION DEVICE, INFERENCE PROGRAM, AND INFERENCE DEVICE

    公开(公告)号:EP4443339A1

    公开(公告)日:2024-10-09

    申请号:EP22922194.0

    申请日:2022-12-22

    申请人: OMRON Corporation

    IPC分类号: G06N3/09 G06N3/04

    CPC分类号: G06N3/04 G06N20/00 G06N3/09

    摘要: The model generation device performs machine learning for an inference model that includes a preprocessing module and a graph inference module. The preprocessing module includes a feature extractor and a selection module. The feature extractor calculates a feature value of each element belonging to one of a plurality of sets included in the input graph. The selection module selects one or more edges extending from each element as a starting point based on the calculated feature value of each element, and generates graph information, indicating the calculated feature value of each element and an edge selection result for each set. The graph inference module is configured to be differentiable and infers a solution to a task for the input graph from the generated graph information for each set.

    IMAGE CLASSIFICATION METHOD AND APPARATUS, COMPUTER DEVICE, AND STORAGE MEDIUM

    公开(公告)号:EP4394726A1

    公开(公告)日:2024-07-03

    申请号:EP22909548.4

    申请日:2022-11-04

    摘要: Embodiments of the present application relate to the technical field of computers, and disclosed are an image classification method and apparatus, a computer device, and a storage medium. The method comprises: obtaining image features of a pathological image to be classified; for each scale in a plurality of scales, extracting local features corresponding to the scale from the image features; performing splicing processing according to the local features corresponding to each scale to obtain a spliced image feature; and classifying the spliced image feature to obtain a category to which the pathological image belongs. According to the method provided by the embodiments of the present application, local features corresponding to different scales contain different information, so that the finally obtained spliced image feature contains feature information corresponding to different scales, and the feature information of the spliced image feature is enriched; and the category to which the pathological image belongs is determined on the basis of the spliced image feature, so that the accuracy of the category is ensured.