Statistical Comparisons of the Top 10 Algorithms in Data Mining for Classification Task

This work is builds on the study of the 10 top data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) community in December 2006. We address the same study, but with the application of statistical tests to establish, a more appropriate and justified ranking classifier for classification tasks. Current studies and practices on theoretical and empirical comparison of several methods, approaches, advocated tests that are more appropriate. Thereby, recent studies recommend a set of simple and robust non-parametric tests for statistical comparisons classifiers. In this paper, we propose to perform non-parametric statistical tests by the Friedman test with post-hoc tests corresponding to the comparison of several classifiers on multiple data sets. The tests provide a better judge for the relevance of these algorithms

Les Forêts Aléatoires en Apprentissage Semi-Supervisé (Co-forest) pour la segmentation des images rétiniennes

Nous proposons une approche qui permet la reconnaissance automatique des régions Disques et Cups pour la mesure du rapport CDR (Cup/Disc Ratio) par apprentissage semi-supervisé. Une étude comparative de plusieurs techniques est proposée. Le principe repose sur une croissance de région en classifiant les pixels voisins à partir des pixels d'intérêt de l'image par apprentissage semi-supervisé. Les points d'intérêt sont détectés par l'algorithme Fuzzy C-means (FCM)

Reinforced confidence in self-training for a semi-supervised medical data classification

International audienc

An optimised pixel-based classification approach for automatic white blood cells segmentation

International audienceThe pixel-based classification is an automatic approach for classifying all pixels in the image but does not take into account the spatial information for the region of interest. On the other hand, region-growing methods take into account the neighbourhood pixels information. However, in region-growing methods, a pixel-group called 'points of interest' are needed to initialise the growing process. In this paper, we proposed an optimised pixel-based classification by the cooperation of region growing strategy. This original segmentation scheme is performed in two phases for the automatic recognition of white blood cells (WBC): the first is a learning step with colour characteristics of each pixel in the image. The second is a region growing application by classifying neighbouring pixels from pixels of interest extracted by the ultimate erosion technique. This process has proved that the cooperation allows obtaining a nucleus and cytoplasm segmentation as closer to what as expected in the reference images

Influence of normalization and color features on super-pixel classification: application to cytological image segmentation

International audienc

Semi-supervised superpixel classification for medical images segmentation: application to detection of glaucoma disease

International audienc

Semi-Supervised learning with Collaborative Bagged Multi-label K-Nearest-Neighbors

Over the last few years, Multi-label classification has received significant attention from researchers to solve many issues in many fields. The manual annotation of available datasets is time-consuming and need a huge effort from the expert, especially for Multi-label applications in which each example of learning is associated with many labels at once. To overcome the manual annotation drawback, and to take advantages from the large amounts of unlabeled data, many semi-supervised approaches were proposed in the literature to give more sophisticated and fast solutions to support the automatic labeling of the unlabeled data. In this paper, a Collaborative Bagged Multi-label K-Nearest-Neighbors (CobMLKNN) algorithm is proposed, that extend the co-Training paradigm by a Multi-label K-Nearest-Neighbors algorithm. Experiments on ten real-world Multi-label datasets show the effectiveness of CobMLKNN algorithm to improve the performance of MLKNN to learn from a small number of labeled samples by exploiting unlabeled samples

Highly Performing Automatic Detection of Structural Chromosomal Abnormalities Using Siamese Architecture

The detection of structural chromosomal abnormalities (SCA) is crucial for diagnosis, prognosis and management of many genetic diseases and cancers. This detection, done by highly qualified medical experts, is tedious and time-consuming. We propose a highly performing and intelligent method to assist cytogeneticists to screen for SCA. Each chromosome is present in two copies that make up a pair of chromosomes. Usually, SCA are present in only one copy of the pair. Convolutional neural networks (CNN) with Siamese architecture are particularly relevant for evaluating similarities between two images, which is why we used this method to detect abnormalities between both chromosomes of a given pair. As a proof-of-concept, we first focused on a deletion occurring on chromosome 5 (del(5q)) observed in hematological malignancies. Using our dataset, we conducted several experiments without and with data augmentation on seven popular CNN models. Overall, performances obtained were very relevant for detecting deletions, particularly with Xception and InceptionResNetV2 models achieving and of -score, respectively. We additionally demonstrated that these models successfully recognized another SCA, inversion inv(3), which is one of the most difficult SCA to detect. The performance improved when the training was applied on inversion inv(3) dataset, achieving of -score. The technique that we propose in this paper is the first highly performing method based on Siamese architecture that allows the detection of SCA. Our code is publicly available at: https://github.com/MEABECHAR/ChromosomeSiameseAD