Social-sparsity brain decoders: faster spatial sparsity

Spatially-sparse predictors are good models for brain decoding: they give accurate predictions and their weight maps are interpretable as they focus on a small number of regions. However, the state of the art, based on total variation or graph-net, is computationally costly. Here we introduce sparsity in the local neighborhood of each voxel with social-sparsity, a structured shrinkage operator. We find that, on brain imaging classification problems, social-sparsity performs almost as well as total-variation models and better than graph-net, for a fraction of the computational cost. It also very clearly outlines predictive regions. We give details of the model and the algorithm.Comment: in Pattern Recognition in NeuroImaging, Jun 2016, Trento, Italy. 201

HRF estimation improves sensitivity of fMRI encoding and decoding models

Extracting activation patterns from functional Magnetic Resonance Images (fMRI) datasets remains challenging in rapid-event designs due to the inherent delay of blood oxygen level-dependent (BOLD) signal. The general linear model (GLM) allows to estimate the activation from a design matrix and a fixed hemodynamic response function (HRF). However, the HRF is known to vary substantially between subjects and brain regions. In this paper, we propose a model for jointly estimating the hemodynamic response function (HRF) and the activation patterns via a low-rank representation of task effects.This model is based on the linearity assumption behind the GLM and can be computed using standard gradient-based solvers. We use the activation patterns computed by our model as input data for encoding and decoding studies and report performance improvement in both settings.Comment: 3nd International Workshop on Pattern Recognition in NeuroImaging (2013

Excited states of the free excitons in CuInSe2 single crystals

High-quality CuInSe2 single crystals were studied using polarization resolved photoluminescence (PL) and magnetophotoluminescence (MPL). The emission lines related to the first (n=2) excited states for the A and B free excitons were observed in the PL and MPL spectra at 1.0481 meV and 1.0516 meV, respectively. The spectral positions of these lines were used to estimate accurate values for the A and B exciton binding energies (8.5 meV and 8.4 meV, respectively), Bohr radii (7.5 nm), band gaps (E-g(A)=1.050 eV and E-g(B)=1.054 eV), and the static dielectric constant (11.3) assuming the hydrogenic model

Mycophenolic acid and 6-mercaptopurine both inhibit B-cell proliferation in granulomatosis with polyangiitis patients, whereas only mycophenolic acid inhibits B-cell IL-6 production

Granulomatosis with polyangiitis (GPA) is an autoimmune disease affecting mainly small blood vessels. B-cells are important in the GPA pathogenesis as precursors of autoantibody-producing cells but likely also contribute (auto)antibody-independently. This has been underlined by the effectiveness of B-cell-depletion (with Rituximab) in inducing and maintaining disease remission. Mycophenolate-mofetil (MMF) and azathioprine (AZA) are immunosuppressive therapies frequently used in GPA-patients. Interestingly, MMF-treated GPA-patients are more prone to relapses than AZA-treated patients, while little is known about the influence of these drugs on B-cells. We investigated whether MMF or AZA treatment (or their active compounds) alters the circulating B-cell subset distribution and has differential effects on in vitro B-cell proliferation and cytokine production in GPA-patients that might underlie the different relapse rate. Circulating B-cell subset frequencies were determined in samples from AZA-treated (n = 13), MMF-treated (n = 12), untreated GPA-patients (n = 19) and matched HCs (n = 41). To determine the ex vivo effects of the active compounds of MMF and AZA, MPA and 6-MP respectively, on B-cell proliferation and cytokine production, PBMCs of untreated GPA-patients (n = 29) and matched HCs (n = 30) were cultured for 3-days in the presence of CpG-oligodeoxynucleotides (CpG) with MPA or 6-MP. After restimulation (with phorbol myristate acetate, calcium-ionophore), cytokine-positive B-cell frequencies were measured. Finally, to assess the effect of MMF or AZA treatment on in vitro B-cell proliferation and cytokine production, PBMCs of MMF-treated (n = 18), and AZA-treated patients (n = 28) and HCs (n = 41) were cultured with CpG. The memory B-cell frequency was increased in AZA- compared to MMF-treated patients, while no other subset was different. The active compounds of MMF and AZA showed in vitro that MPA decreased B-cell proliferation in GPA-patients and HCs. B-cell proliferation in MMF- and AZA-treated patients was not different. Finally, the IL-6+ B-cell frequency was decreased by MPA compared to 6-MP. No differences in IL-10+, IL-6+ or TNFα+ B-cell proportions or proliferation were found in MMF- and AZA-treated patients. Our results indicate that MMF could be superior to AZA in inhibiting B-cell cytokine production in GPA-patients. Future studies should assess the effects of these immunosuppressive drugs on other immune cells to elucidate mechanisms underlying the potential differences in relapse rates

Total Variation meets Sparsity: statistical learning with segmenting penalties

International audiencePrediction from medical images is a valuable aid to diagnosis. For instance, anatomical MR images can reveal certain disease conditions, while their functional counterparts can predict neuropsychi-atric phenotypes. However, a physician will not rely on predictions by black-box models: understanding the anatomical or functional features that underpin decision is critical. Generally, the weight vectors of clas-sifiers are not easily amenable to such an examination: Often there is no apparent structure. Indeed, this is not only a prediction task, but also an inverse problem that calls for adequate regularization. We address this challenge by introducing a convex region-selecting penalty. Our penalty combines total-variation regularization, enforcing spatial conti-guity, and 1 regularization, enforcing sparsity, into one group: Voxels are either active with non-zero spatial derivative or zero with inactive spatial derivative. This leads to segmenting contiguous spatial regions (inside which the signal can vary freely) against a background of zeros. Such segmentation of medical images in a target-informed manner is an important analysis tool. On several prediction problems from brain MRI, the penalty shows good segmentation. Given the size of medical images, computational efficiency is key. Keeping this in mind, we contribute an efficient optimization scheme that brings significant computational gains

Cosmology from Galaxy Redshift Surveys with PointNet

In recent years, deep learning approaches have achieved state-of-the-art results in the analysis of point cloud data. In cosmology, galaxy redshift surveys resemble such a permutation invariant collection of positions in space. These surveys have so far mostly been analysed with two-point statistics, such as power spectra and correlation functions. The usage of these summary statistics is best justified on large scales, where the density field is linear and Gaussian. However, in light of the increased precision expected from upcoming surveys, the analysis of -- intrinsically non-Gaussian -- small angular separations represents an appealing avenue to better constrain cosmological parameters. In this work, we aim to improve upon two-point statistics by employing a \textit{PointNet}-like neural network to regress the values of the cosmological parameters directly from point cloud data. Our implementation of PointNets can analyse inputs of $\mathcal{O}(10^4) - \mathcal{O}(10^5)$ galaxies at a time, which improves upon earlier work for this application by roughly two orders of magnitude. Additionally, we demonstrate the ability to analyse galaxy redshift survey data on the lightcone, as opposed to previously static simulation boxes at a given fixed redshift

Solid Spherical Energy (SSE) CNNs for Efficient 3D Medical Image Analysis

Invariance to local rotation, to differentiate from the global rotation of images and objects, is required in various texture analysis problems. It has led to several breakthrough methods such as local binary patterns, maximum response and steerable filterbanks. In particular, textures in medical images often exhibit local structures at arbitrary orientations. Locally Rotation Invariant (LRI) Convolutional Neural Networks (CNN) were recently proposed using 3D steerable filters to combine LRI with Directional Sensitivity (DS). The steerability avoids the expensive cost of convolutions with rotated kernels and comes with a parametric representation that results in a drastic reduction of the number of trainable parameters. Yet, the potential bottleneck (memory and computation) of this approach lies in the necessity to recombine responses for a set of predefined discretized orientations. In this paper, we propose to calculate invariants from the responses to the set of spherical harmonics projected onto 3D kernels in the form of a lightweight Solid Spherical Energy (SSE) CNN. It offers a compromise between the high kernel specificity of the LRI-CNN and a low memory/operations requirement. The computational gain is evaluated on 3D synthetic and pulmonary nodule classification experiments. The performance of the proposed approach is compared with steerable LRI-CNNs and standard 3D CNNs, showing competitive results with the state of the art

Channel selection for test-time adaptation under distribution shift

To ensure robustness and generalization to real-world scenarios, test-time adaptation has been recently studied as an approach to adjust models to a new data distribution during inference. Test-time batch normalization is a simple and popular method that achieved compelling performance on domain shift benchmarks by recalculating batch normalization statistics on test batches. However, in many practical applications this technique is vulnerable to label distribution shifts. We propose to tackle this challenge by only selectively adapting channels in a deep network, minimizing drastic adaptation that is sensitive to label shifts. We find that adapted models significantly improve the performance compared to the baseline models and counteract unknown label shifts