Multiple instance learning: attention to instance classification

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00383614" target="_blank" >RIV/68407700:21230/25:00383614 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1117/12.3045059" target="_blank" >https://doi.org/10.1117/12.3045059</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1117/12.3045059" target="_blank" >10.1117/12.3045059</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Multiple instance learning: attention to instance classification

Popis výsledku v původním jazyce

Multiple instance learning (MIL) is a specific form of weakly-supervised learning where instances with hidden labels are grouped into bags, and only bag labels are observed. MIL models generally fall into one of two classes, focusing on instance or bag classification. Blurring the line between the two classes, an existing attention-based MIL method classifies bags accurately while indicating key instances. We build upon this method and propose to jointly learn a bag and instance classifier, essentially removing the distinction between bag-centric and instance-centric approaches. We performed experiments on the CAMELYON16 dataset of histopathological images and two other image datasets. The experiments showed that our method achieves high bag-level performance, comparable to other competing MIL methods. At the same time, our method outperforms other MIL methods in instance-level classification and, when provided with enough data, achieves results comparable to supervised learning using instance labels.

Název v anglickém jazyce

Multiple instance learning: attention to instance classification

Popis výsledku anglicky

Multiple instance learning (MIL) is a specific form of weakly-supervised learning where instances with hidden labels are grouped into bags, and only bag labels are observed. MIL models generally fall into one of two classes, focusing on instance or bag classification. Blurring the line between the two classes, an existing attention-based MIL method classifies bags accurately while indicating key instances. We build upon this method and propose to jointly learn a bag and instance classifier, essentially removing the distinction between bag-centric and instance-centric approaches. We performed experiments on the CAMELYON16 dataset of histopathological images and two other image datasets. The experiments showed that our method achieves high bag-level performance, comparable to other competing MIL methods. At the same time, our method outperforms other MIL methods in instance-level classification and, when provided with enough data, achieves results comparable to supervised learning using instance labels.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

SPIE Medical Imaging 2025: Image Processing

ISBN

9781510685901

ISSN

1605-7422

e-ISSN

—

Počet stran výsledku

9

Strana od-do

—

Název nakladatele

SPIE

Místo vydání

Bellingham (stát Washington)

Místo konání akce

San Diego, California,

Datum konání akce

16. 2. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001487072200029