Mohamed Mejri

Mohamed Mejri, Aymen Mejri, Oumayma Mejri et al.

Breast cancer is one of the factors that cause the increase of mortality of women. The most widely used method for diagnosing this geological disease i.e. breast cancer is the ultrasound scan. Several key features such as the smoothness and the texture of the tumor captured through ultrasound scans encode the abnormality of the breast tumors (malignant from benign). However, ultrasound scans are often noisy and include irrelevant parts of the breast that may bias the segmentation of eventual tumors. In this paper, we are going to extract the region of interest ( i.e, bounding boxes of the tumors) and feed-forward them to one semantic segmentation encoder-decoder structure based on its classification (i.e, malignant or benign). the whole process aims to build an instance-based segmenter from a semantic segmenter and an object detector.

Mejri Mohamed, Antoine Richard, Cedric Pradalier

In the industry, the value of wood-logs strongly depends on their internal structure and more specifically on the knots' distribution inside the trees. As of today, CT-scanners are the prevalent tool to acquire accurate images of the trees internal structure. However, CT-scanners are expensive, and slow, making their use impractical for most industrial applications. Knowing where the knots are within a tree could improve the efficiency of the overall tree industry by reducing waste and improving the quality of wood-logs by-products. In this paper we evaluate different deep-learning based architectures to predict the internal knots distribution of a tree from its outer-shape, something that has never been done before. Three types of techniques based on Convolutional Neural Networks (CNN) will be studied. The architectures are tested on both real and synthetic CT-scanned trees. With these experiments, we demonstrate that CNNs can be used to predict internal knots distribution based on the external surface of the trees. The goal being to show that these inexpensive and fast methods could be used to replace the CT-scanners. Additionally, we look into the performance of several off-the-shelf object-detectors to detect knots inside CT-scanned images. This method is used to autonomously label part of our real CT-scanned trees alleviating the need to manually segment the whole of the images.

Mohamed Mejri, Antoine Richard, Cédric Pradalier

The analysis of the internal structure of trees is highly important for both forest experts, biological scientists, and the wood industry. Traditionally, CT-scanners are considered as the most efficient way to get an accurate inner representation of the tree. However, this method requires an important investment and reduces the cost-effectiveness of this operation. Our goal is to design neural-network-based methods to predict the internal density of the tree from its external bark shape. This paper compares different image-to-image(2D), volume-to-volume(3D) and Convolutional Long Short Term Memory based neural network architectures in the context of the prediction of the defect distribution inside trees from their external bark shape. Those models are trained on a synthetic dataset of 1800 CT-scanned look-like volumetric structures of the internal density of the trees and their corresponding external surface.