A new methodology to predict the oncotype scores based on clinico-pathological data with similar tumor profiles

no code implementations • 13 Mar 2023 • Zeina Al Masry, Romain Pic, Clément Dombry, Christine Devalland

This methodology can be used particularly to identify the patients of the training cohort that share similarities with the new patient and to predict an estimate of the distribution of the ODX score.

Decision Making

A DEEP LEARNING PIPELINE FOR BREAST CANCER KI-67PROLIFERATION INDEX SCORING

no code implementations • arXiv 2022 • Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, L.H MOUSS, Noureddine Zerhouni

The Ki-67 proliferation index is an essential biomarker that helps pathologists to diagnose andselect appropriate treatments.

Semantic Segmentation

A deep learning pipeline for breast cancer ki-67 proliferation index scoring

no code implementations • 14 Mar 2022 • Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, L. H Mouss, Noureddine Zerhouni

The extracted nuclei are then divided into overlapped and non-overlapped regions based on eight geometric and statistical features.

Semantic Segmentation

Data Labeling Impact on Deep Learning Models in Digital Pathology: a Breast Cancer Case Study

no code implementations • Studies in Autonomic, Data-driven and Industrial Computing 2022 • Khaled Benaggoune, Zeina Al Masry, Jian Ma, Christine Devalland, S. VALMARY-DEGANO, L.H MOUSS, Noureddine Zerhouni

The aim of this study is to highlight the impact of data labeling on deep learning models.

Spitzoid Lesions Diagnosis based on GA feature selection and Random Forest

no code implementations • 10 Mar 2020 • Abir Belaala, Labib Sadek, Noureddine Zerhouni, Christine Devalland

A private spitzoid lesions dataset have been used to evaluate the system proposed in this study.

feature selection General Classification +1