Abstract: A medical image region screening method and apparatus and a storage medium are provided. The method includes: obtaining a medical image of biological tissue, segmenting tissue regions of a plurality of tissue types from the medical image, selecting, from the tissue regions of the plurality of tissue types based on types of capturing positions of the medical image, a reserved region, obtaining a positional relationship between the reserved region and a predicted lesion region in the medical image; and screening for the predicted lesion region in the medical image based on the positional relationship, to obtain a target lesion region.

Abstract: A computer device, obtains a mammographic image of a unilateral breast. The mammographic image includes a cranial-caudal (CC)-position mammographic image and a mediolateral-oblique (MLO)-position mammographic image. The computer device invokes a breast detection model to perform a prediction of a condition of the unilateral breast according to the CC-position mammographic image and the MLO-position mammographic image. The device obtains a prediction result of the unilateral breast, and generates and outputs a detection report that includes the prediction result.

Abstract: Provided are an artificial intelligence (AI)-based method and apparatus for training a classification task model, a device, and a storage medium, which relate to the field of machine learning (ML) technologies. The method includes: training an initial feature extractor by using a first dataset to obtain a feature extractor, the first dataset being a class imbalanced dataset; constructing a generative adversarial network, the generative adversarial network including the feature extractor and an initial feature generator; training the generative adversarial network by using second class samples to obtain a feature generator; constructing a classification task model, the classification task model including the feature generator and the feature extractor; and training the classification task model by using the first dataset, the feature generator being configured to augment the second class samples in a feature space in a training process of the classification task model.

Abstract: An image processing method performed by a terminal is provided. A molybdenum target image is obtained, and a plurality of candidate regions are extracted from the molybdenum target image. In the molybdenum target image, a target region is marked in the plurality of candidate regions by using a neural network model obtained by deep learning training, a probability that a lump comprised in the target region is a target lump being greater than a first threshold, a probability that the target lump is a malignant tumor being greater than a second threshold, and the neural network model being used for indicating a mapping relationship between a candidate region and a probability that a lump comprised in the candidate region is the target lump.