New coronal pneumonia segmentation method based on weak supervision multi-task learning

The invention discloses a new coronal pneumonia focus segmentation method based on weak supervision multi-task learning, and mainly solves the problems of large image difference and poor segmentation result of patients with different clinical grades in the existing method. According to the scheme, the method comprises the following steps: acquiring CT image data of a new coronal pneumonia patient, performing resampling and histogram matching, and dividing a training set, a verification set and a test set; designing a multi-scale convolution module HMS to replace a convolution layer in the last two coding layers of an existing 3D ResUNet segmentation network, adding a classification network in the network, and constructing a new coronal pneumonia focus segmentation network based on weak supervision multi-task learning; training the network by using the training set and selecting a trained model with the best effect by using the verification set; and inputting the test set into the final trained model to obtain