MFGAN: A Lightweight Fast Multi-task Multi-scale Feature-fusion Model based on GAN

Cell segmentation and counting is a time-consuming task and an important experimental step in traditional biomedical research. Many current counting methods require exact cell locations. However, there are few such cell datasets with detailed object coordinates. Most existing cell datasets only have the total number of cells and a global segmentation labelling. To make more effective use of existing datasets, we divided the cell counting task into cell number prediction and cell segmentation respectively. This paper proposed a lightweight fast multi-task multi-scale feature fusion model based on generative adversarial networks (MFGAN). To coordinate the learning of these two tasks, we proposed a Combined Hybrid Loss function (CH Loss) and used conditional GAN to train our network. We proposed a Lightweight Fast Multitask Generator (LFMG) which reduced the number of parameters by 20% compared with U-Net but got better performance on cell segmentation. We used multi-scale feature fusion technology to improve the quality of reconstructed segmentation images. In addition, we also proposed a Structure Fusion Discrimination (SFD) to refine the accuracy of the details of the features. Our method achieved non-Point-based counting that no longer needs to annotate the exact position of each cell in the image during the training and successfully achieved excellent results on cell counting and cell segmentation.