# HPS-Net: Multi-Task Network for Medical Image Segmentation with Predictable Performance
###### Abstract  
In recent years, medical image segmentation (MIS) has made a huge breakthrough due to the success of deep learning. However, the existing MIS algorithms still suffer from two types of uncertainties: (1) the uncertainty of the plausible segmentation hypotheses and (2) the uncertainty of segmentation performance. These two types of uncertainties affect the effectiveness of the MIS algorithm and then affect the reliability of medical diagnosis. Many studies have been done on the former but ignore the latter. Therefore, we proposed the hierarchical predictable segmentation network (HPS-Net), which consists of a new network structure, a new loss function, and a cooperative training mode. According to our knowledge, HPS-Net is the first network in the MIS area that can generate both the diverse segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses and the measure predictions about these hypotheses to avoid the uncertainty of segmentation performance. Extensive experiments were conducted on the LIDC-IDRI dataset and the ISIC2018 dataset. The results show that HPS-Net has the highest Dice score compared with the benchmark methods, which means it has the best segmentation performance. The results also confirmed that the proposed HPS-Net can effectively predict TNR and TPR. s _symmetry_  
Article  
H.W. in practice. Most automatic segmentation algorithms treat the segmentation target as a one-to-one mapping from the image to the output mask. However, only providing the most likely segmentation hypothesis is not enough for radiologists. Radiologists need all plausible segmentation hypotheses to avoid the uncertainty of the plausible segmentation hypotheses. They also need predictable statistical measures about these hypotheses to assist them in evaluating the segmentation performance. If possible, radiologists even want to use their specified measurement values to generate segmentation hypotheses. Many studies have been done to solve the uncertainty of the plausible segmentation hypotheses by producing more than one plausible hypothesis from the models. [PERSON] et al. [3] propose to learn an ensemble of deep models to generate multiple plausible hypotheses. However, the output segmentations generated by the ensembles lack diversity, and the ensembles usually do not learn rare segmentation hypotheses, because their models are trained independently, and the impact of the rare segmentation hypotheses is weakened by the majority. [PERSON] et al. [4] present a max-margin formulation and the oracle set loss to directly model the M-Best prediction problem. Enlightened by [PERSON] et al. [4], [PERSON] et al. [5] and [PERSON] et al. [6] combined the oracle set loss with a common deep network with M heads to generate M hypotheses. Nevertheless, all the above-mentioned methods can only generate a fixed number of hypotheses and are not graceful to extend to a large number of hypotheses. This problem can be solved by a conditional variational autoencoder (CVAE) [7], as a CVAE can effectively perform probabilistic inference by learning Gaussian latent variables to model complex distributions. With the learned conditional distribution, a CVAE can generate unlimited and diverse segmentation hypotheses. Due to the excellent segmentation performance of the U-Net [8], [PERSON] et al. [9] combined the CVAE with the U-Net. The combined method is called probabilistic U-net, which can produce an unlimited number of segmentation hypotheses while delivering better performance than network ensembles [3] and M-heads [5; 6]. Recently, probabilistic hierarchical segmentation (PHiSeg) [10] further extends the prior net and the posterior net of the probabilistic from a single resolution structure to a hierarchical multi-resolution structure and achieves state-of-the-art performance across multiple datasets. As another type of uncertainty, the uncertainty of segmentation performance has not drawn enough attention.