Consiglio Nazionale delle Ricerche

Tipo di prodottoArticolo in rivista
TitoloGigapixel Histopathological Image Analysis using Attention-based Neural Networks
Anno di pubblicazione2021
Formato-
Autore/iBrancati N., De Pietro G., Frucci M., Riccio D.
Affiliazioni autoriICAR-CNR
Autori CNR e affiliazioni
  • NADIA BRANCATI
  • DANIEL RICCIO
  • GIUSEPPE DE PIETRO
  • MARIA FRUCCI
Lingua/e
  • inglese
AbstractAlthough CNNs are widely considered as the state-of-the-art models in various applications of image analysis, one of the main challenges still open is the training of a CNN on high resolution images. Different strategies have been proposed involving either a rescaling of the image or an individual processing of parts of the image. Such strategies cannot be applied to images, such as gigapixel histopathological images, for which a high reduction in resolution inherently effects a loss of discriminative information, and in respect of which the analysis of single parts of the image suffers from a lack of global information or implies a high workload in terms of annotating the training images in such a way as to select significant parts. We propose a method for the analysis of gigapixel histopathological images solely by using weak image-level labels. In particular, two analysis tasks are taken into account: a binary classification and a prediction of the tumor proliferation score. Our method is based on a CNN structure consisting of a compressing path and a learning path. In the compressing path, the gigapixel image is packed into a grid-based feature map by using a residual network devoted to the feature extraction of each patch into which the image has been divided. In the learning path, attention modules are applied to the grid-based feature map, taking into account spatial correlations of neighboring patch features to find regions of interest, which are then used for the final whole slide analysis. Our method integrates both global and local information, is flexible with regard to the size of the input images and only requires weak image-level labels. Comparisons with different methods of the state-of-the-art on two well known datasets, Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed model.
Lingua abstractinglese
Altro abstract-
Lingua altro abstract-
Pagine da87552
Pagine a87562
Pagine totali11
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 rivista9
Fascicolo della rivista-
DOI10.1109/ACCESS.2021.3086892
Verificato da refereeSì: Internazionale
Stato della pubblicazionePublished version
Indicizzazione (in banche dati controllate)-
Parole chiaveHistopathological Image, deep learning, Attention-based CNN
Link (URL, URI)https://ieeexplore.ieee.org/document/9447746
Titolo parallelo-
Licenza-
Scadenza embargo-
Data di accettazione-
Note/Altre informazioni-
Strutture CNR
  • ICAR — Istituto di calcolo e reti ad alte prestazioni
Moduli/Attività/Sottoprogetti CNR
  • DIT.AD022.156.002 : GRUPPI DI RICERCA - Napoli
Progetti Europei-
Allegati
Gigapixel Histopathological Image Analysis UsingAttention-Based Neural Networks
Descrizione: versione pubblicata del paper
Tipo documento: application/pdf