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How deeply to fine-tune a convolutional neural network : a case study using a histopathology dataset
ID Kandel, Ibrahem (Author), ID Castelli, Mauro (Author)

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Abstract
Accurate classification of medical images is of great importance for correct disease diagnosis. The automation of medical image classification is of great necessity because it can provide a second opinion or even a better classification in case of a shortage of experienced medical staff. Convolutional neural networks (CNN) were introduced to improve the image classification domain by eliminating the need to manually select which features to use to classify images. Training CNN from scratch requires very large annotated datasets that are scarce in the medical field. Transfer learning of CNN weights from another large non-medical dataset can help overcome the problem of medical image scarcity. Transfer learning consists of fine-tuning CNN layers to suit the new dataset. The main questions when using transfer learning are how deeply to fine-tune the network and what difference in generalization that will make. In this paper, all of the experiments were done on two histopathology datasets using three state-of-the-art architectures to systematically study the effect of block-wise fine-tuning of CNN. Results show that fine-tuning the entire network is not always the best option; especially for shallow networks, alternatively fine-tuning the top blocks can save both time and computational power and produce more robust classifiers.

Language:English
Work type:Article
Typology:1.01 - Original Scientific Article
Organization:EF - School of Economics and Business
Publication version:Version of Record
Year:2020
Number of pages:Str. 1-20
Numbering:Vol. 10, iss. 10 (art. 3359)
PID:20.500.12556/RUL-124232 This link opens in a new window
UDC:004:78
ISSN on article:2076-3417
DOI:10.3390/app10103359 This link opens in a new window
COBISS.SI-ID:38418947 This link opens in a new window
Publication date in RUL:11.01.2021
Views:857
Downloads:205
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Record is a part of a journal

Title:Applied sciences
Shortened title:Appl. sci.
Publisher:MDPI
ISSN:2076-3417
COBISS.SI-ID:522979353 This link opens in a new window

Licences

License:CC BY 4.0, Creative Commons Attribution 4.0 International
Link:http://creativecommons.org/licenses/by/4.0/
Description:This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:11.01.2021

Projects

Funder:ARRS - Slovenian Research Agency
Project number:P5-0410
Name:Digitalizacija kot gonilo trajnostnega razvoja posameznika, organizacij in družbe

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0022/2018
Name:GADgET

Funder:FCT - Fundação para a Ciência e a Tecnologia, I.P.
Project number:DSAIPA/DS/0113/2019
Name:AICE

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