Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection

↓ Direkt till sidans innehåll
↓ Direkt till sidans sekundära innehåll (sidomenyn)

Search: onr:"swepub:oai:DiVA.org:hj-64171" > Deep multiple insta...

1 of 1
Previous record
Next record
To hitlist

Details
MARC

Koriakina, Nadezhda,1991-Uppsala universitet,Bildanalys och människa-datorinteraktion,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden (author)

Deep multiple instance learning versus conventional deep single instance learning for interpretable oral cancer detection

Article/chapterEnglish2024

Publisher, publication year, extent ...

Public Library of Science (PLoS),2024
printrdacarrier

Numbers

LIBRIS-ID:oai:DiVA.org:hj-64171
https://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-64171URI
https://doi.org/10.1371/journal.pone.0302169DOI
https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-527711URI

Supplementary language notes

Language:English
Summary in:English

Part of subdatabase

SwePubSwePub

Classification

Subject category:ref swepub-contenttype
Subject category:art swepub-publicationtype

Notes

The current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample taken from the oral cavity. This process is time-consuming and more invasive than an alternative approach of acquiring a brush sample followed by cytological analysis. Using a microscope, skilled cytotechnologists are able to detect changes due to malignancy; however, introducing this approach into clinical routine is associated with challenges such as a lack of resources and experts. To design a trustworthy OC detection system that can assist cytotechnologists, we are interested in deep learning based methods that can reliably detect cancer, given only per-patient labels (thereby minimizing annotation bias), and also provide information regarding which cells are most relevant for the diagnosis (thereby enabling supervision and understanding). In this study, we perform a comparison of two approaches suitable for OC detection and interpretation: (i) conventional single instance learning (SIL) approach and (ii) a modern multiple instance learning (MIL) method. To facilitate systematic evaluation of the considered approaches, we, in addition to a real OC dataset with patient-level ground truth annotations, also introduce a synthetic dataset—PAP-QMNIST. This dataset shares several properties of OC data, such as image size and large and varied number of instances per bag, and may therefore act as a proxy model of a real OC dataset, while, in contrast to OC data, it offers reliable per-instance ground truth, as defined by design. PAP-QMNIST has the additional advantage of being visually interpretable for non-experts, which simplifies analysis of the behavior of methods. For both OC and PAP-QMNIST data, we evaluate performance of the methods utilizing three different neural network architectures. Our study indicates, somewhat surprisingly, that on both synthetic and real data, the performance of the SIL approach is better or equal to the performance of the MIL approach. Visual examination by cytotechnologist indicates that the methods manage to identify cells which deviate from normality, including malignant cells as well as those suspicious for dysplasia. We share the code as open source.

Subject headings and genre

MEDICIN OCH HÄLSOVETENSKAP Klinisk medicin Cancer och onkologi hsv//swe
MEDICAL AND HEALTH SCIENCES Clinical Medicine Cancer and Oncology hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datavetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Sciences hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Annan data- och informationsvetenskap hsv//swe
NATURAL SCIENCES Computer and Information Sciences Other Computer and Information Science hsv//eng
TEKNIK OCH TEKNOLOGIER Medicinteknik Medicinsk bildbehandling hsv//swe
ENGINEERING AND TECHNOLOGY Medical Engineering Medical Image Processing hsv//eng
NATURVETENSKAP Data- och informationsvetenskap Datorseende och robotik hsv//swe
NATURAL SCIENCES Computer and Information Sciences Computer Vision and Robotics hsv//eng
Deep Learning
Humans
Mouth Neoplasms
Neural Networks
Computer
Article
artificial neural network
cancer cell
cancer diagnosis
conventional deep single instance learning
cytotechnologist
deep multiple instance learning
human
lenet
machine learning
mouth cancer
resnet18
squeezenet
comparative study
mouth tumor
pathology
Computerized Image Processing

Added entries (persons, corporate bodies, meetings, titles ...)

Sladoje, NatašaUppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden(Swepub:uu)namat934 (author)
Basic, VladimirJönköping University,HHJ, Avdelningen för klinisk diagnostik,HHJ. Studies on Integrated Health and Welfare (SIHW)(Swepub:hj)basvla (author)
Lindblad, JoakimUppsala universitet,Bildanalys och människa-datorinteraktion,Avdelningen Vi3,Centre for Image Analysis, Department of Information Technology, Uppsala University, Uppsala, Sweden(Swepub:uu)joali534 (author)
Uppsala universitetBildanalys och människa-datorinteraktion (creator_code:org_t)

Related titles

In:PLOS ONE: Public Library of Science (PLoS)19:4 April1932-6203

Internet link

Find in a library

PLOS ONE (Search for host publication in LIBRIS)

To the university's database

1 of 1
Previous record
Next record
To hitlist

Find more in SwePub

By the author/editor: Koriakina, Nadez ...; Sladoje, Nataša; Basic, Vladimir; Lindblad, Joakim

About the subject

MEDICAL AND HEALTH SCIENCES: MEDICAL AND HEAL ...; and Clinical Medicin ...; and Cancer and Oncol ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Science ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Other Computer a ...

ENGINEERING AND TECHNOLOGY: ENGINEERING AND ...; and Medical Engineer ...; and Medical Image Pr ...

NATURAL SCIENCES: NATURAL SCIENCES; and Computer and Inf ...; and Computer Vision ...

Articles in the publication: PLOS ONE

By the university: Jönköping University; Uppsala University

Search outside SwePub

Extend your search to:: Google; Google Book Search; Google Scholar

Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.

LIBRIS.kb.se