EBAT: Enhanced Bidirectional and Autoregressive Transformers for Removing Hairs in Hairy Dermoscopic Images

Abstract

A great progress in deep learning technologies for skin cancer detection from dermoscopic images has been made for a decade. While its performance is vulnerable to a large amount of hairs densely covering the skin surface, the existing image processing methods frequently fail to remove hairs in hairy skin images. In this paper, we propose, as a deep learning approach to removing hairs, a generative image inpainting network where bidirectional autoregressive transformers (BATs) are employed to learn image features and are systematically integrated with convolutional neural networks (CNNs) in multiple spatial scales in order to reconstruct missing regions. Each patch split from a masked image is unfolded and processed through BATs, and re-folded to constitute diverse shapes of feature maps through kernel-based unfolding-folding operations. By introducing the multi-scale features extracted by collaborative learning of transformers and CNNs to the texture generator network, our method can effectively reconstruct minute details of local regions as well as global structure which might not be easily inferred from neighbor pixels in hairy skin images. Quantitative and qualitative evaluations show not only that our multi-scale dual-modality strategy is much robust to reconstruct hair-shaped missing regions compared to the existing transformer-based image inpainting method called BAT-Fill, but also that our framework outperforms the state-of-the-art image inpainting models in removing hairs from hairy dermoscopic images.

Keywords

• Hair removal
• skin image
• image inpainting
• transformer
• deep learning
• generative adversarial networks