Exploring Hyperspectral Histopathology Image Segmentation from A Deformable Perspective

Hyperspectral images (HSIs) offer great potential for computational pathology. However, limited by the spectral redundancy and the lack of spectral prior in popular 2D networks, previous HSI based techniques do not perform well. To address these problems, we propose to segment HSIs from a deformable perspective, which processes different spectral bands independently and fuses spatiospectral features of interest via deformable attention mechanisms. In addition, we propose Deformable Self-Supervised Spectral Regression (DF-S3R), which introduces two self-supervised pre-text tasks based on the low rank prior of HSIs enabling the network learning with spectrum-related features. During pre-training, DF-S3R learns both spectral structures and spatial morphology, and the jointly pre-trained architectures help alleviate the transfer risk to downstream fine-tuning. Compared to previous works, experiments show that our deformable architecture and pre-training method perform much better than other competitive methods on pathological semantic segmentation tasks, and the visualizations indicate that our method can trace the critical spectral characteristics from subtle spectral disparities. Code will be released at https://github.com/Ayakax/DFS3R.