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

01/03/2022
by   Xingyu Li, et al.
15

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 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.

READ FULL TEXT

page 20

page 23

page 24

research
10/11/2017

Convolutional Neural Networks for Histopathology Image Classification: Training vs. Using Pre-Trained Networks

We explore the problem of classification within a medical image data-set...
research
02/10/2016

Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

Remarkable progress has been made in image recognition, primarily due to...
research
05/05/2020

Multi-task pre-training of deep neural networks for digital pathology

In this work, we investigate multi-task learning as a way of pre-trainin...
research
10/29/2020

Speech-Image Semantic Alignment Does Not Depend on Any Prior Classification Tasks

Semantically-aligned (speech, image) datasets can be used to explore "vi...
research
02/28/2018

Fine-grained wound tissue analysis using deep neural network

Tissue assessment for chronic wounds is the basis of wound grading and s...
research
06/13/2021

HistoTransfer: Understanding Transfer Learning for Histopathology

Advancement in digital pathology and artificial intelligence has enabled...

Please sign up or login with your details

Forgot password? Click here to reset