System and method for automatically identifying mitosis in h&e stained breast cancer pathological images

A system for automatically identifying mitosis in H&E stained breast cancer pathological images, comprising:

• 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 and rotation;
• the original picture obtains a mask binary image through a pixel position labeling;
• obtaining training data of a segmentation network:
• using a data set labeled at a pixel level, obtaining the mask binary image corresponding to it through the pixel position labeling, and cutting a corresponding RGB image according to a mask to obtain corresponding mitotic patches and non-mitotic patches as the training data;
• a segmentation module:
• training the 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;
• a classification module:
• respectively training three classification networks for a classification and performing a decision-making level fusion on results of the three networks, using a voting method to obtain a final output result, and then restoring and marking a final result to obtain a final detection result;
• the segmentation module comprises an attention segmentation network module and a candidate cell selection method module;
• the classification module comprises a three-branch classification network module, a decision-making fusion algorithm module and a classification result marking module;
• and
• the attention segmentation network module introduces an SE module:
• constructing a segmentation network based on Attention U-Net, combining an attention mechanism to realize a weighted extraction of different features in spaces and channels, aiming at morphological features of research objectives mitotic cells, improving a squeeze operation in the SE module, and taking into account both texture information and background information;
• and based on above characteristics, enabling a segmentation network SEAttUnet to realize a redistribution of attentions of space positions in different channels and feature maps.