# Improving Feature Extraction from Histopathological Images Through A Fine-tuning ImageNet Model

arXiv: https://arxiv.org/abs/2201.00636 · HTML: https://mlsanity.com/paper/2201.00636/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 3 Jan 2022.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Whole Slide Images based Cancer Survival Prediction using Attention Guided Deep Multiple Instance Learning Networks](https://mlsanity.com/paper/2009.11169/) (2020)
- [Deep Learning for Identifying Metastatic Breast Cancer](https://mlsanity.com/paper/1606.05718/) (2016)
- [Fine-Tuning and Training of DenseNet for Histopathology Image Representation Using TCGA Diagnostic Slides](https://mlsanity.com/paper/2101.07903/) (2021)
- [Self-Supervision Closes the Gap Between Weak and Strong Supervision in Histology](https://mlsanity.com/paper/2012.03583/) (2020)
- [Improving Prostate Cancer Detection with Breast Histopathology Images](https://mlsanity.com/paper/1903.05769/) (2019)
- [How Much Off-The-Shelf Knowledge Is Transferable From Natural Images To Pathology Images?](https://mlsanity.com/paper/2005.01609/) (2020)

## Built on by (most cited first, 8 of 8)

- [A robust and lightweight deep attention multiple instance learning algorithm for predicting genetic alterations](https://mlsanity.com/paper/2206.00455/) (2022)
- [Colorectal cancer survival prediction using deep distribution based multiple-instance learning](https://mlsanity.com/paper/2204.11294/) (2022)
- [Deep Learning-based Prediction of Breast Cancer Tumor and Immune Phenotypes from Histopathology](https://mlsanity.com/paper/2404.16397/) (2024)
- [Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort](https://mlsanity.com/paper/2409.01330/) (2024)
- [Optimize Deep Learning Models for Prediction of Gene Mutations Using Unsupervised Clustering](https://mlsanity.com/paper/2204.01593/) (2022)
- [Time to Embrace Natural Language Processing (NLP)-based Digital Pathology: Benchmarking NLP- and Convolutional Neural Network-based Deep Learning Pipelines](https://mlsanity.com/paper/2302.10406/) (2023)
- [Glioma subtype classification from histopathological images using in-domain and out-of-domain transfer learning: An experimental study](https://mlsanity.com/paper/2309.17223/) (2023)
- [Overfitting in Histopathology Model Training: The Need for Customized Architectures](https://mlsanity.com/paper/2506.16631/) (2025)

## Abstract (arXiv)

Due to lack of annotated pathological images, transfer learning has been the predominant approach in the field of digital pathology.Pre-trained neural networks based on ImageNet database are often used to extract "off the shelf" features, achieving great success in predicting tissue types, molecular features, and clinical outcomes, etc. We hypothesize that fine-tuning the pre-trained models using histopathological images could further improve feature extraction, and downstream prediction performance.We used 100,000 annotated HE image patches for colorectal cancer (CRC) to finetune a pretrained Xception model via a twostep approach.The features extracted from finetuned Xception (FTX2048) model and Imagepretrained (IMGNET2048) model were compared through: (1) tissue classification for HE images from CRC, same image type that was used for finetuning; (2) prediction of immunerelated gene expression and (3) gene mutations for lung adenocarcinoma (LUAD).Fivefold cross validation was used for model performance evaluation. The extracted features from the finetuned FTX2048 exhibited significantly higher accuracy for predicting tisue types of CRC compared to the off the shelf feature directly from Xception based on ImageNet database. Particularly, FTX2048 markedly improved the accuracy for stroma from 87% to 94%. Similarly, features from FTX2048 boosted the prediction of transcriptomic expression of immunerelated genesin LUAD. For the genes that had signigicant relationships with image fetures, the features fgrom the finetuned model imprroved the prediction for the majority of the genes. Inaddition, fetures from FTX2048 improved prediction of mutation for 5 out of 9 most frequently mutated genes in LUAD.