Lesion Segmentation Framework Based on Convolutional Neural Networks with Dual Attention Mechanism

Abstract

Computational intelligence has been widely used in medical information processing. The deep learning methods, especially, have many successful applications in medical image analysis. In this paper, we proposed an end-to-end medical lesion segmentation framework based on convolutional neural networks with a dual attention mechanism, which integrates both fully and weakly supervised segmentation. The weakly supervised segmentation module achieves accurate lesion segmentation by using bounding-box labels of lesion areas, which solves the problem of the high cost of pixel-level labels with lesions in the medical images. In addition, a dual attention mechanism is introduced to enhance the network’s ability for visual feature learning. The dual attention mechanism (channel and spatial attention) can help the network pay attention to feature extraction from important regions. Compared with the current mainstream method of weakly supervised segmentation using pseudo labels, it can greatly reduce the gaps between ground-truth labels and pseudo labels. The final experimental results show that our proposed framework achieved more competitive performances on oral lesion dataset, and our framework further extended to dermatological lesion segmentation.

Enhancing Skin Disease Segmentation with Weighted Ensemble Region-Based Convolutional Network

“…To ensure accurate results, the input data undergo a preprocessing method. This is necessary because the skin images taken may contain unwanted elements like noise, poor background, and low illumination [17]. By applying preprocessing techniques to the raw data, the proposed method can achieve better performance accuracy.…”