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Ismael Kone
Publications - 5
Citations - 377
Ismael Kone is an academic researcher. The author has contributed to research in topics: Breast cancer & Contextual image classification. The author has an hindex of 3, co-authored 5 publications receiving 236 citations.
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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.
Book ChapterDOI
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Ismael Kone,Lahsen Boulmane +1 more
TL;DR: A hierarchical system of convolutional neural networks that classifies automatically patches of microscopic histology image analysis into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma is proposed.
Book ChapterDOI
Hierarchical ResNeXt Models for Breast Cancer Histology Image Classification
Ismael Kone,Lahsen Boulmane +1 more
TL;DR: In this article, a hierarchical system of convolutional neural networks (CNN) was proposed to classify patches of these images into four pathologies: normal, benign, in situ carcinoma and invasive carcinoma.
Posted Content
Hybrid Forests for Left Ventricle Segmentation using only the first slice label
Ismael Kone,Lahsen Boulmane +1 more
TL;DR: In this article, the authors proposed a segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task, where only the first slice needs to be manually labeled to train the model which then infers the next slice's segmentation.
Proceedings ArticleDOI
Hybrid forests for left ventricle segmentation using only the first slice label
Ismael Kone,Lahsen Boulmane +1 more
TL;DR: A segmentation method which exploits MRI images sequential structure to nearly drop out this labeling task and can be applied on human left ventricle segmentation and results are very promising.