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Chapter and Conference Paper
Relieving Pixel-Wise Labeling Effort for Pathology Image Segmentation with Self-training
Data scarcity is a common issue when training deep learning models for digital pathology, as large exhaustively-annotated image datasets are difficult to obtain. In this paper, we propose a self-training based...
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Chapter and Conference Paper
Empirical Evaluation of Deep Learning Approaches for Landmark Detection in Fish Bioimages
In this paper we perform an empirical evaluation of variants of deep learning methods to automatically localize anatomical landmarks in bioimages of fishes acquired using different imaging modalities (microsco...
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Chapter and Conference Paper
Deep Learning Approaches for Head and Operculum Segmentation in Zebrafish Microscopy Images
In this paper, we propose variants of deep learning methods to segment head and operculum of the zebrafish larvae in microscopy images. In the first approach, we used a three-class model to jointly segment hea...
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Article
Open AccessLandmark detection in 2D bioimages for geometric morphometrics: a multi-resolution tree-based approach
The detection of anatomical landmarks in bioimages is a necessary but tedious step for geometric morphometrics studies in many research domains. We propose variants of a multi-resolution tree-based approach to...
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Article
Open AccessEvaluation of BRCA1-related molecular features and microRNAs as prognostic factors for triple negative breast cancers
The BRCA1 gene plays a key role in triple negative breast cancers (TNBCs), in which its expression can be lost by multiple mechanisms: germinal mutation followed by deletion of the second allele; negative regulat...
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Article
Open AccessA rich internet application for remote visualization and collaborative annotation of digital slides in histology and cytology
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Article
Open AccessHigh-density lipoprotein proteome dynamics in human endotoxemia
A large variety of proteins involved in inflammation, coagulation, lipid-oxidation and lipid metabolism have been associated with high-density lipoprotein (HDL) and it is anticipated that changes in the HDL pr...
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Chapter and Conference Paper
Automatic Localization of Interest Points in Zebrafish Images with Tree-Based Methods
In many biological studies, scientists assess effects of experimental conditions by visual inspection of microscopy images. They are able to observe whether a protein is expressed or not, if cells are going th...
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Article
Oligodendrocyte development and myelinogenesis are not impaired by high concentrations of phenylalanine or its metabolites
Phenylketonuria (PKU) is a metabolic genetic disease characterized by deficient phenylalanine hydroxylase (PAH) enzymatic activity. Brain hypomyelination has been reported in untreated patients, but its mechan...
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Article
Open AccessRandom subwindows and extremely randomized trees for image classification in cell biology
With the improvements in biosensors and high-throughput image acquisition technologies, life science laboratories are able to perform an increasing number of experiments that involve the generation of a large ...
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Chapter and Conference Paper
Content-Based Image Retrieval by Indexing Random Subwindows with Randomized Trees
We propose a new method for content-based image retrieval which exploits the similarity measure and indexing structure of totally randomized tree ensembles induced from a set of subwindows randomly extracted f...
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Chapter and Conference Paper
Reinforcement Learning with Raw Image Pixels as Input State
We report in this paper some positive simulation results obtained when image pixels are directly used as input state of a reinforcement learning algorithm. The reinforcement learning algorithm chosen to carry ...
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Chapter and Conference Paper
Biomedical Image Classification with Random Subwindows and Decision Trees
In this paper, we address a problem of biomedical image classification that involves the automatic classification of x-ray images in 57 predefined classes with large intra-class variability. To achieve that go...
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Chapter and Conference Paper
A Comparison of Generic Machine Learning Algorithms for Image Classification
In this paper, we evaluate 7 machine learning algorithms for image classification including our recent approach that combines building of ensembles of extremely randomized trees and extraction of sub-windows f...
