Feature learning and clustering analysis for images classification

The problem this thesis is addressing is to improve an existing classification in 10 categories of the images captured by SEM microscopes. In particular, the challenge faced is to classify those images according to a hierarchical tree structure of sub-categories without requiring any further human labelling effort. In order to uncover intrinsic structures among the images, a procedure involving supervised and unsupervised feature learning, as well as cluster analysis is defined. Moreover, to reduce the bias introduced in the supervised phase, various strategies focusing on features of different nature and level of abstraction are analyzed

Deep Learning, Feature Learning, and Clustering Analysis for SEM Image Classification

In this paper, we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope (SEM). This is done by coupling supervised and unsupervised learning approaches. We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy. Then, we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales (from 1 $\mu$m to 2 $\mu$m). Finally, we compare different clustering methods to uncover intrinsic structures in the images

NeuroQuantify -- An Image Analysis Software for Detection and Quantification of Neurons and Neurites using Deep Learning

The segmentation of cells and neurites in microscopy images of neuronal networks provides valuable quantitative information about neuron growth and neuronal differentiation, including the number of cells, neurites, neurite length and neurite orientation. This information is essential for assessing the development of neuronal networks in response to extracellular stimuli, which is useful for studying neuronal structures, for example, the study of neurodegenerative diseases and pharmaceuticals. However, automatic and accurate analysis of neuronal structures from phase contrast images has remained challenging. To address this, we have developed NeuroQuantify, an open-source software that uses deep learning to efficiently and quickly segment cells and neurites in phase contrast microscopy images. NeuroQuantify offers several key features: (i) automatic detection of cells and neurites; (ii) post-processing of the images for the quantitative neurite length measurement based on segmentation of phase contrast microscopy images, and (iii) identification of neurite orientations. The user-friendly NeuroQuantify software can be installed and freely downloaded from GitHub https://github.com/StanleyZ0528/neural-image-segmentation

Fibrés vectoriels semistables sur des arbres de bulles

La (semi)stabilité, introduite par Mumford en 1963, sert à la construction d'espaces de modules de fibrés vectoriels par les méthodes de GIT. Dans la frontière de l'espace de modules compactifié apparaissent des faisceaux non localement libres. La thèse vise à proposer un nouveau stock d'objets de frontière plus maniables, dans le cas de dimension 2 et de rang 2, qui sont des fibrés sur des arbres de bulles A ayant S comme racine. La motivation vient de la théorie de jauge et de l'étude par Nagaraj-Seshadri et Teixidor i Bigas des fibrés sur des courbes réductibles. La semistabilité sur A dépend d'une polarisation, c'est à dire, d'un fibré en droites ample. Le domaine des paramètres de la polarisation est bien plus petit et les fibrés semistables sont plus rares en dimension 2 que dans le cas de courbes. Pour certaines polarisations, on donne des critères de semistabilité des fibrés sur A en fonction de leurs restrictions aux composantes de A. Bien que les faisceaux étudiés sur A soient des fibrés, leur sous-faisceaux potentiellement déstabilisants peuvent être juste réflexifs. On entreprend alors la classification des faisceaux réflexifs sur des arbres de bulles, basée sur les travaux de Burban-Drozd. On étudie ensuite les déformations des fibrés arboriformes. Le résultat principal est qu'un fibré stable sur A, pour certaines polarisations, est toujours la limite de fibrés stables sur S. Enfin, on compare le stock des fibrés stables arboriformes, limites d'instantons de charge 2 sur le plan projectif, avec celui de Markushevich-Tikhomirov-Trautmann, obtenu par une autre approche.The (semi)stability, introduced by Mumford in 1963, was used for construction of moduli spaces of vector bundles by methods of GIT. In the boundary of the compactified moduli space appear non locally free sheaves. The thesis aims to propose a new stock of more manageable boundary objects, in the case of dimension 2 and rank 2, which are bundles on bubble trees A having S as root. Motivation comes from gauge theory and the study of bundles on reducible curves by Nagaraj-Seshadri and Teixidor i Bigas.The semistability on A depends on polarization, that is, on an ample line bundle. The domain of parameters of polarization is much smaller, and semistable bundles are more scarce in dimension 2 than in the case of curves. For certain polarizations, semistability criteria for bundles on A are given in terms of their restrictions to the components of A. Although the sheaves studied on A are bundles, their potentially destabilizing subsheaves can be just reflexive. Thence the classification of reflexive sheaves on bubble trees is undertaken, basing upon the work of Burban-Drozd. Next the deformations of tree-like bundles are studied. The main result is that a stable bundle on A, for certain polarizations, is always the limit of stable bundles on S. Finally, a comparison is made between the stock of stable tree-like bundles which are limits of instantons of charge 2 on the projective plane, and the one of Markushevich-Tikhomirov-Trautmann, obtained by a completely different approach

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

International audienc

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

International audienc

Pushing the Limits of Exoplanet Discovery via Direct Imaging with Deep Learning

International audienc