Abstract: Embodiments of this application disclose a tumor cell content evaluation method. A digital pathology slide image is obtained, and an effective pathological region is determined based on the digital pathology slide image; a tumor cell region corresponding to the effective pathological region is identified by using a deep learning-based pathology image classifier; and tumor cell content of the digital pathology slide image is determined based on the tumor cell region according to a preset evaluation rule. In this way, the tumor cell content of the digital pathology slide image is automatically evaluated, and accuracy and objectivity of evaluating the tumor cell content are improved. In addition, a tumor cell content evaluation system, a computer device, and a storage medium are further provided.

Abstract: A method for identifying a target region of a digital pathology slide, including: obtaining a scanned image of a pathology slide; inputting the scanned image of the pathology slide into a preset deep learning-based identification model; extracting a contour feature of the scanned image of the pathology slide by using an image contour feature extraction submodel, to obtain a contour image; segmenting the contour image by using an image segmentation submodel to obtain a plurality of sub-contour images; separately performing classification and identification on the plurality of sub-contour images by using an image classification submodel, to obtain a region category corresponding to each sub-contour image; and determining a target region image based on the region category of each sub-contour image. In addition, a system for identifying a target region of a digital pathology slide, a device, and a medium are further proposed.

Abstract: Disclosed herein are a method and a device for pathologically labeling medical images, which is capable of effectively solving the problem associated with limitations on working location of artificial labeling, enhancing labeling efficiency, and providing a huge number of learning samples with high quality with artificial intelligence.

Abstract: Disclosed herein are a method and a device for pathologically labeling medical images, which is capable of effectively solving the problem associated with limitations on working location of artificial labeling, enhancing labeling efficiency, and providing a huge number of learning samples with high quality with artificial intelligence.