Inferring Skin Lesion Segmentation With Fully Connected Crfs Based On Multiple Deep Convolutional Neural Networks

This article presents a method to infer skin lesion segmentation based on multiple deep convolutional neural network (DCNN) models by employing fully connected conditional random fields (CRFs). This method is on the strength of the synergism between ensemble learning which is responsible for introducing diversity from multiple DCNN models and CRFs inference which is in charge of probabilistic inference based on random fields over dermoscopy images. Contrasting to single DCNN models, the proposed method can gain better segmentation by comprehensively utilizing the advances and performance preferences of multiple different DCNN models. In comparison with simple ensemble schemes, it can effectively and precisely refine the fuzzy lesion boundary by utilizing the information in test images to maximize label agreement between similar pixels. Further, an engineering bonus is the feasibility of parallelization for the heavy operation, predicting on multiple DCNN models. In experiments, we tested the effectiveness and robustness of the proposed method on the mainstream datasets ISIC 2017 and PH2, and the results were competitive with the state-of-art methods. we also confirmed that the proposed method can capture the local information in fuzzy dermoscopy images being able to find more accurate lesion borders with a good boost on Boundary Recall (BR) metric. Moreover, since the hyper-parameters in CRFs are explainable, it is possible to adjust them manually to reach better results case by case, being attractive in practice. This work is of value on integration between the deep learning technologies and probabilistic inference in resolving lesion segmentation, and has great potential to be applied in similar tasks.