Deep-learning based colorectal cancer pathological analysis with hyperspectral light field microscopy

5D hyperspectral light field (H-LF) integrates multi-angular and multi-spectral observation, offering a comprehensive opportunity to capture more detailed information from biological samples. In this article, we integrate hyperspectral light field microscopy imaging to analyze H&E-stained whole slide images (WSIs) of colorectal cancer (CRC). Specifically, we design a triple separable transformer encoder (HLFTST) that efficiently extracts features by decoupling the 5D H-LF data into lower-dimensional components and applying self-attention for global interaction. We also introduce a text encoder-decoder to align H-LF features with language, enabling automatic cell classification and pathology report generation through a three-stage training pipeline. Experiments show our method outperforms 2D, 3D, and 4D baselines, improving precision by up to 4.88% and F1 score by 4.21% across five CRC cell categories. Additionally, it generates meaningful pathology descriptions, highlighting its potential for enhancing diagnostics and supporting personalized treatment in broader biomedical settings.