M
Maria Frucci
Researcher at Indian Council of Agricultural Research
Publications - 67
Citations - 1249
Maria Frucci is an academic researcher from Indian Council of Agricultural Research. The author has contributed to research in topics: Image segmentation & Segmentation. The author has an hindex of 16, co-authored 67 publications receiving 885 citations. Previous affiliations of Maria Frucci include Institute for High Performance Computing and Networking, CNR & National Research Council.
Papers
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Journal ArticleDOI
BACH: Grand challenge on breast cancer histology images.
Guilherme Aresta,Teresa Araújo,Scotty Kwok,Sai Saketh Chennamsetty,Mohammed Safwan,Varghese Alex,Bahram Marami,Marcel Prastawa,Monica Chan,Michael J. Donovan,Gerardo Fernandez,Jack Zeineh,Matthias Kohl,Christoph Walz,Florian Ludwig,Stefan Braunewell,Maximilian Baust,Quoc Dang Vu,Minh Nguyen Nhat To,Eal Kim,Jin Tae Kwak,Sameh Galal,Veronica Sanchez-Freire,Nadia Brancati,Maria Frucci,Daniel Riccio,Yaqi Wang,Lingling Sun,Kaiqiang Ma,Jiannan Fang,Ismael Kone,Lahsen Boulmane,Aurélio Campilho,Catarina Eloy,António Polónia,Paulo Aguiar +35 more
TL;DR: The Grand Challenge on Breast Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018) as mentioned in this paper.
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A Deep Learning Approach for Breast Invasive Ductal Carcinoma Detection and Lymphoma Multi-Classification in Histological Images
TL;DR: This paper explores deep learning methods for the automatic analysis of Hematoxylin and Eosin stained histological images of breast cancer and lymphoma and proposes a deep learning approach for two different use cases: the detection of invasive ductal carcinoma in breast histology images and the classification of lymphoma sub-types.
Journal ArticleDOI
Human skin detection through correlation rules between the YCb and YCr subspaces based on dynamic color clustering
TL;DR: Comparisons with six well-known rule-based methods in literature carried out on four publicly available databases show that the proposed method outperforms the others in terms of quantitative performance evaluation parameters.
Posted Content
Hierarchical Graph Representations in Digital Pathology
Pushpak Pati,Guillaume Jaume,Antonio Foncubierta,Florinda Feroce,Anna Maria Anniciello,Giosuè Scognamiglio,Nadia Brancati,Maryse Fiche,Estelle Dubruc,Daniel Riccio,Maurizio Di Bonito,Giuseppe De Pietro,Gerardo Botti,Jean-Philippe Thiran,Maria Frucci,Orcun Goksel,Maria Gabrani +16 more
TL;DR: A novel multi-level hierarchical entity-graph representation of tissue specimens is proposed to model the hierarchical compositions that encode histological entities as well as their intra- and inter-entity level interactions and is demonstrated to yield superior classification results compared to alternative methods aswell as individual pathologists.
Journal ArticleDOI
Hierarchical graph representations in digital pathology
Pushpak Pati,Guillaume Jaume,Antonio Foncubierta-Rodríguez,Florinda Feroce,Anna Maria Anniciello,Giosuè Scognamiglio,Nadia Brancati,Maryse Fiche,Estelle Dubruc,Daniel Riccio,Maurizio Di Bonito,Giuseppe De Pietro,Gerardo Botti,Jean-Philippe Thiran,Maria Frucci,Orcun Goksel,Maria Gabrani +16 more
TL;DR: In this article, a hierarchical graph neural network is proposed to operate on the hierarchical entity-graph and map the tissue structure to tissue functionality, treating the tissue as a hierarchical composition of multiple types of histological entities from fine to coarse level.