Abstract: Provided is a weakly supervised pathological image tissue segmentation method based on an online noise suppression strategy, including: acquiring a hematoxylin-eosin (H&E) stained graph, processing the H&E stained graph to obtain a data set, dividing the data set, training a classification network based on a divided data set, and generating a pseudo-label; suppressing a noise existing in the pseudo-label based on the online noise suppression strategy, and training a semantic segmentation network through the pseudo-label after noise suppression and a training set corresponding to the pseudo-label to obtain a prediction result of the semantic segmentation network after the training, and taking the prediction result as a final segmentation result.

Abstract: A system and a method for automatically identifying mitosis in H&E stained breast cancer pathological images are provided, belonging to the technical field of digital image processing, and including an input image preprocessing module: cutting an original picture according to a predetermined patch size, and performing a data enhancement by means of picture flipping, rotation, and the like; and a segmentation module: training a segmentation network by cutting patches in a training set, cutting data of a test set according to a corresponding size and sending to the segmentation network to obtain a patch-level segmentation result, and then reconstructing a segmented result into an image belonging to an original size according to patch coordinate information intercepted in a preprocessing stage of it.