Publications by authors named "I Bousaid"
Background: The need for developing new biomarkers is increasing with the emergence of many targeted therapies. Artificial Intelligence (AI) algorithms have shown great promise in the medical imaging field to build predictive models. We developed a prognostic model for solid tumour patients using AI on multimodal data.
View Article and Find Full Text PDFPurpose: The purpose of this study was to create a deep learning algorithm to infer the benign or malignant nature of breast nodules using two-dimensional B-mode ultrasound data initially marked as BI-RADS 3 and 4.
Materials And Methods: An ensemble of mask region-based convolutional neural networks (Mask-RCNN) combining nodule segmentation and classification were trained to explicitly localize the nodule and generate a probability of the nodule to be malignant on two-dimensional B-mode ultrasound. These probabilities were aggregated at test time to produce final results.
Three artificial intelligence data challenges based on CT and ultrasound.
Diagn Interv Imaging
November 2021
Purpose: The 2020 edition of these Data Challenges was organized by the French Society of Radiology (SFR), from September 28 to September 30, 2020. The goals were to propose innovative artificial intelligence solutions for the current relevant problems in radiology and to build a large database of multimodal medical images of ultrasound and computed tomography (CT) on these subjects from several French radiology centers.
Materials And Methods: This year the attempt was to create data challenge objectives in line with the clinical routine of radiologists, with less preprocessing of data and annotation, leaving a large part of the preprocessing task to the participating teams.
Article Synopsis
- The study aimed to create an algorithm using convolutional neural networks (CNN) that can automatically estimate coronary artery calcium (CAC) from unenhanced ECG-gated CT scans.
- Researchers trained a CNN with a 3D U-Net architecture on 783 CT scans to detect and segment calcifications, calculating the Agatston score and comparing it to radiologist assessments.
- The final model achieved a high accuracy (C-index of 0.951), although it struggled with small or low-density calcifications near the mitral valve, potentially enhancing workflow by automating the CAC scoring process.
Purpose: The purpose of this study was to develop a fast and automatic algorithm to detect and segment lymphadenopathy from head and neck computed tomography (CT) examination.
Materials And Methods: An ensemble of three convolutional neural networks (CNNs) based on a U-Net architecture were trained to segment the lymphadenopathies in a fully supervised framework. The resulting predictions were assessed using the Dice similarity coefficient (DSC) on examinations presenting one or more adenopathies.