# S3R: Self-supervised Spectral Regression for Hyperspectral Histopathology Image Classification

arXiv: https://arxiv.org/abs/2209.08770 · HTML: https://mlsanity.com/paper/2209.08770/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 23 references that are in the map.
- First posted on arXiv 19 Sep 2022.

## Builds on (most cited first, 15 of 15)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Emerging Properties in Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.14294/) (2021)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [An Empirical Study of Training Self-Supervised Vision Transformers](https://mlsanity.com/paper/2104.02057/) (2021)
- [SimMIM: A Simple Framework for Masked Image Modeling](https://mlsanity.com/paper/2111.09886/) (2021)
- [Masked Feature Prediction for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2112.09133/) (2021)
- [Corrupted Image Modeling for Self-Supervised Visual Pre-Training](https://mlsanity.com/paper/2202.03382/) (2022)
- [Self-supervised driven consistency training for annotation efficient histopathology image analysis](https://mlsanity.com/paper/2102.03897/) (2021)
- [FastHyMix: Fast and Parameter-free Hyperspectral Image Mixed Noise Removal](https://mlsanity.com/paper/2109.08879/) (2021)

## Built on by (most cited first, 2 of 2)

- [A Hyperspectral Imaging Dataset and Methodology for Intraoperative Pixel-Wise Classification of Metastatic Colon Cancer in the Liver](https://mlsanity.com/paper/2411.06969/) (2024)
- [Deep Learning Approaches for Medical Imaging Under Varying Degrees of Label Availability: A Comprehensive Survey](https://mlsanity.com/paper/2504.11588/) (2025)

## Abstract (arXiv)

Benefited from the rich and detailed spectral information in hyperspectral images (HSI), HSI offers great potential for a wide variety of medical applications such as computational pathology. But, the lack of adequate annotated data and the high spatiospectral dimensions of HSIs usually make classification networks prone to overfit. Thus, learning a general representation which can be transferred to the downstream tasks is imperative. To our knowledge, no appropriate self-supervised pre-training method has been designed for histopathology HSIs. In this paper, we introduce an efficient and effective Self-supervised Spectral Regression (S3R) method, which exploits the low rank characteristic in the spectral domain of HSI. More concretely, we propose to learn a set of linear coefficients that can be used to represent one band by the remaining bands via masking out these bands. Then, the band is restored by using the learned coefficients to reweight the remaining bands. Two pre-text tasks are designed: (1)S3R-CR, which regresses the linear coefficients, so that the pre-trained model understands the inherent structures of HSIs and the pathological characteristics of different morphologies; (2)S3R-BR, which regresses the missing band, making the model to learn the holistic semantics of HSIs. Compared to prior arts i.e., contrastive learning methods, which focuses on natural images, S3R converges at least 3 times faster, and achieves significant improvements up to 14% in accuracy when transferring to HSI classification tasks.