Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloA Deep Learning approach for breast invasive ductal carcinoma detection and lymphoma multi-classification in histological images
Anno di pubblicazione2019
FormatoElettronico
Autore/iBrancati N., De Pietro G., Frucci M. and Riccio D.
Affiliazioni autoriICAR-CNR
Autori CNR e affiliazioni
  • NADIA BRANCATI
  • DANIEL RICCIO
  • GIUSEPPE DE PIETRO
  • MARIA FRUCCI
Lingua/e
  • inglese
AbstractAccurately identifying and categorizing cancer structures/sub-types in histological images is an important clinical task involving a considerable workload and a specific subspecialty of pathologists. Digitizing pathology is a current trend that provides large amounts of visual data allowing a faster and more precise diagnosis through the development of automatic image analysis techniques. Recent studies have shown promising results for the automatic analysis of cancer tissue by using deep learning strategies that automatically extract and organize the discriminative information from the data. This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types. Both use cases have been addressed by adopting a Residual Convolutional Neural Network which is part of a Convolutional Autoencoder Network (i.e. FusionNet). The performances have been evaluated on public datasets of digital histological images and have been compared with those obtained by using different deep neural networks (UNet and ResNet). Additionally, comparisons with the state of the art have been considered, in accordance with different deep learning approaches. The experimental results show an improvement of 5:06% in F-measure score for the detection task, and an improvement of 1:09% in the accuracy measure for the classification task.
Lingua abstractinglese
Altro abstract-
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Pagine da44709
Pagine a44720
Pagine totali12
RivistaIEEE access
Attiva dal 2013
Editore: Institute of Electrical and Electronics Engineers - Piscataway, NJ
Paese di pubblicazione: Stati Uniti d'America
Lingua: inglese
ISSN: 2169-3536
Titolo chiave: IEEE access
Titolo proprio: IEEE access
Numero volume della rivista7
Fascicolo della rivista1
DOI10.1109/ACCESS.2019.2908724
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiavehistological images, deep learning, multiclassification, detection
Link (URL, URI)https://ieeexplore.ieee.org/document/8678759
Titolo parallelo-
Licenza-
Scadenza embargo-
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Note/Altre informazioni-
Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD022.050.001 : Sistemi Cognitivi
Progetti Europei-
Allegati
A Deep Learning approach for breast invasive ductal carcinoma detection and lymphoma multi-classification in histological images
Descrizione: This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma. In particular, a deep learning approach is proposed for two different use cases: the detection of invasive ductal carcinoma in breast histological images and the classification of lymphoma sub-types
Tipo documento: application/pdf