# ISG: I can See Your Gene Expression

arXiv: https://arxiv.org/abs/2210.16728 · HTML: https://mlsanity.com/paper/2210.16728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 41 references that are in the map.
- First posted on arXiv 30 Oct 2022.

## Builds on (most cited first, 24 of 24)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Exploring Simple Siamese Representation Learning](https://mlsanity.com/paper/2011.10566/) (2020)
- [Barlow Twins: Self-Supervised Learning via Redundancy Reduction](https://mlsanity.com/paper/2103.03230/) (2021)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation](https://mlsanity.com/paper/2008.00951/) (2020)
- [StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation](https://mlsanity.com/paper/2011.12799/) (2020)
- [Patch-based Convolutional Neural Network for Whole Slide Tissue Image Classification](https://mlsanity.com/paper/1504.07947/) (2015)
- [CycleMLP: A MLP-like Architecture for Dense Prediction](https://mlsanity.com/paper/2107.10224/) (2021)
- [MPViT: Multi-Path Vision Transformer for Dense Prediction](https://mlsanity.com/paper/2112.11010/) (2021)
- [Vision Transformer with Progressive Sampling](https://mlsanity.com/paper/2108.01684/) (2021)
- [Dense Extreme Inception Network for Edge Detection](https://mlsanity.com/paper/2112.02250/) (2021)
- [Instance Segmentation of Biological Images Using Harmonic Embeddings](https://mlsanity.com/paper/1904.05257/) (2019)
- [Relighting Images in the Wild with a Self-Supervised Siamese Auto-Encoder](https://mlsanity.com/paper/2012.06444/) (2020)
- [BI-GCN: Boundary-Aware Input-Dependent Graph Convolution Network for Biomedical Image Segmentation](https://mlsanity.com/paper/2110.14775/) (2021)
- [Solo or Ensemble? Choosing a CNN Architecture for Melanoma Classification](https://mlsanity.com/paper/1904.12724/) (2019)
- [All You Need is Color: Image based Spatial Gene Expression Prediction using Neural Stain Learning](https://mlsanity.com/paper/2108.10446/) (2021)
- [S2FGAN: Semantically Aware Interactive Sketch-to-Face Translation](https://mlsanity.com/paper/2011.14785/) (2020)
- [SILT: Self-supervised Lighting Transfer Using Implicit Image Decomposition](https://mlsanity.com/paper/2110.12914/) (2021)

## Abstract (arXiv)

This paper aims to predict gene expression from a histology slide image precisely. Such a slide image has a large resolution and sparsely distributed textures. These obstruct extracting and interpreting discriminative features from the slide image for diverse gene types prediction. Existing gene expression methods mainly use general components to filter textureless regions, extract features, and aggregate features uniformly across regions. However, they ignore gaps and interactions between different image regions and are therefore inferior in the gene expression task. Instead, we present ISG framework that harnesses interactions among discriminative features from texture-abundant regions by three new modules: 1) a Shannon Selection module, based on the Shannon information content and Solomonoff's theory, to filter out textureless image regions; 2) a Feature Extraction network to extract expressive low-dimensional feature representations for efficient region interactions among a high-resolution image; 3) a Dual Attention network attends to regions with desired gene expression features and aggregates them for the prediction task. Extensive experiments on standard benchmark datasets show that the proposed ISG framework outperforms state-of-the-art methods significantly.