Joseph Paul Cohen

Torchmeta: A Meta-Learning library for PyTorch

The constant introduction of standardized benchmarks in the literature has helped accelerating the recent advances in meta-learning research… (see more). They offer a way to get a fair comparison between different algorithms, and the wide range of datasets available allows full control over the complexity of this evaluation. However, for a large majority of code available online, the data pipeline is often specific to one dataset, and testing on another dataset requires significant rework. We introduce Torchmeta, a library built on top of PyTorch that enables seamless and consistent evaluation of meta-learning algorithms on multiple datasets, by providing data-loaders for most of the standard benchmarks in few-shot classification and regression, with a new meta-dataset abstraction. It also features some extensions for PyTorch to simplify the development of models compatible with meta-learning algorithms. The code is available here: this https URL

2019-09-14

ArXiv (preprint)

GradMask: Reduce Overfitting by Regularizing Saliency

Becks Simpson

Francis Dutil

With too few samples or too many model parameters, overfitting can inhibit the ability to generalise predictions to new data. Within medical… (see more) imaging, this can occur when features are incorrectly assigned importance such as distinct hospital specific artifacts, leading to poor performance on a new dataset from a different institution without those features, which is undesirable. Most regularization methods do not explicitly penalize the incorrect association of these features to the target class and hence fail to address this issue. We propose a regularization method, GradMask, which penalizes saliency maps inferred from the classifier gradients when they are not consistent with the lesion segmentation. This prevents non-tumor related features to contribute to the classification of unhealthy samples. We demonstrate that this method can improve test accuracy between 1-3% compared to the baseline without GradMask, showing that it has an impact on reducing overfitting.

2019-04-15

MIDL.io/2019/Conference/Abstract (unknown)

openreview.net

Automated segmentation of cortical layers in BigBrain reveals divergent cortical and laminar thickness gradients in sensory and motor cortices.

Konrad Wagstyl

Guillem Cucurull

Claude Lepage

Sebastian Bludau

Nicola Palomero-Gallagher

L. Lewis

Thomas Funck

Hannah Spitzer

Timo Dicksheid

Paul C Fletcher

Karl Zilles

Katrin Amunts

Alan C. Evans

Abstract Large-scale in vivo neuroimaging datasets offer new possibilities for reliable, well-powered measures of interregional structural d… (see more)ifferences and biomarkers of pathological changes in a wide variety of neurological and psychiatric diseases. However, so far studies have been structurally and functionally imprecise, being unable to relate pathological changes to specific cortical layers or neurobiological processes. We developed artificial neural networks to segment cortical and laminar surfaces in the BigBrain, a 3D histological model of the human brain. We sought to test whether previously-reported thickness gradients, as measured by MRI, in sensory and motor processing cortices, were present in a histological atlas of cortical thickness, and which cortical layers were contributing to these gradients. Identifying common gradients of cortical organisation enables us to meaningfully relate microstructural, macrostructural and functional cortical parameters. Analysis of thickness gradients across sensory cortices, using our fully segmented six-layered model, was consistent with MRI findings, showing increasing thickness moving up the processing hierarchy. In contrast, fronto-motor cortices showed the opposite pattern with changes in thickness of layers III, V and VI being the primary drivers of these gradients. As well as identifying key differences between sensory and motor gradients, our findings show how the use of this laminar atlas offers insights that will be key to linking single-neuron morphological changes, mesoscale cortical layers and macroscale cortical thickness.

2019-03-17

bioRxiv (preprint)

doi.org

BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices

Konrad Wagstyl

Guillem Cucurull

Claude Lepage

Sebastian Bludau

Nicola Palomero-Gallagher

L. Lewis

Thomas Funck

Hannah Spitzer

Timo Dicksheid

Paul C Fletcher

Karl Zilles

Katrin Amunts

Alan C. Evans

Histological atlases of the cerebral cortex, such as those made famous by Brodmann and von Economo, are invaluable for understanding human b… (see more)rain microstructure and its relationship with functional organization in the brain. However, these existing atlases are limited to small numbers of manually annotated samples from a single cerebral hemisphere, measured from 2D histological sections. We present the first whole-brain quantitative 3D laminar atlas of the human cerebral cortex. This atlas was derived from a 3D histological model of the human brain at 20 micron isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically, the cortical layers in both hemispheres. Our approach overcomes many of the historical challenges with measurement of histological thickness in 2D and the resultant laminar atlas provides an unprecedented level of precision and detail. We utilized this BigBrain cortical atlas to test whether previously reported thickness gradients, as measured by MRI in sensory and motor processing cortices, were present in a histological atlas of cortical thickness, and which cortical layers were contributing to these gradients. Cortical thickness increased across sensory processing hierarchies, primarily driven by layers III, V and VI. In contrast, fronto-motor cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness. These findings illustrate how this laminar atlas will provide a link between single-neuron morphology, mesoscale cortical layering, macroscopic cortical thickness and, ultimately, functional neuroanatomy.

2019-03-17

bioRxiv (preprint)

doi.org

BigBrain 3D atlas of cortical layers: Cortical and laminar thickness gradients diverge in sensory and motor cortices

Konrad Wagstyl

Guillem Cucurull

Claude Lepage

Sebastian Bludau

Nicola Palomero-Gallagher

L. Lewis

Thomas Funck

Hannah Spitzer

Timo Dicksheid

Paul C Fletcher

Karl Zilles

Katrin Amunts

Alan C. Evans

Histological atlases of the cerebral cortex, such as those made famous by Brodmann and von Economo, are invaluable for understanding human b… (see more)rain microstructure and its relationship with functional organization in the brain. However, these existing atlases are limited to small numbers of manually annotated samples from a single cerebral hemisphere, measured from 2D histological sections. We present the first whole-brain quantitative 3D laminar atlas of the human cerebral cortex. This atlas was derived from a 3D histological model of the human brain at 20 micron isotropic resolution (BigBrain), using a convolutional neural network to segment, automatically, the cortical layers in both hemispheres. Our approach overcomes many of the historical challenges with measurement of histological thickness in 2D and the resultant laminar atlas provides an unprecedented level of precision and detail. We utilized this BigBrain cortical atlas to test whether previously reported thickness gradients, as measured by MRI in sensory and motor processing cortices, were present in a histological atlas of cortical thickness, and which cortical layers were contributing to these gradients. Cortical thickness increased across sensory processing hierarchies, primarily driven by layers III, V and VI. In contrast, fronto-motor cortices showed the opposite pattern, with decreases in total and pyramidal layer thickness. These findings illustrate how this laminar atlas will provide a link between single-neuron morphology, mesoscale cortical layering, macroscopic cortical thickness and, ultimately, functional neuroanatomy.

2019-03-17

bioRxiv (preprint)

doi.org

Adversarial Domain Adaptation for Stable Brain-Machine Interfaces

Ali Farshchian

Juan A. Gallego

Lee Miller

Sara Solla

Brain-Machine Interfaces (BMIs) have recently emerged as a clinically viable option to restore voluntary movements after paralysis. These de… (see more)vices are based on the ability to extract information about movement intent from neural signals recorded using multi-electrode arrays chronically implanted in the motor cortices of the brain. However, the inherent loss and turnover of recorded neurons requires repeated recalibrations of the interface, which can potentially alter the day-to-day user experience. The resulting need for continued user adaptation interferes with the natural, subconscious use of the BMI. Here, we introduce a new computational approach that decodes movement intent from a low-dimensional latent representation of the neural data. We implement various domain adaptation methods to stabilize the interface over significantly long times. This includes Canonical Correlation Analysis used to align the latent variables across days; this method requires prior point-to-point correspondence of the time series across domains. Alternatively, we match the empirical probability distributions of the latent variables across days through the minimization of their Kullback-Leibler divergence. These two methods provide a significant and comparable improvement in the performance of the interface. However, implementation of an Adversarial Domain Adaptation Network trained to match the empirical probability distribution of the residuals of the reconstructed neural signals outperforms the two methods based on latent variables, while requiring remarkably few data points to solve the domain adaptation problem.

2019-01-01

ICLR.cc/2019/Conference (poster)

openreview.net

A Survey of Mobile Computing for the Visually Impaired

Margaux Luck

The number of visually impaired or blind (VIB) people in the world is estimated at several hundred million. Based on a series of interviews … (see more)with the VIB and developers of assistive technology, this paper provides a survey of machine-learning based mobile applications and identifies the most relevant applications. We discuss the functionality of these apps, how they align with the needs and requirements of the VIB users, and how they can be improved with techniques such as federated learning and model compression. As a result of this study we identify promising future directions of research in mobile perception, micro-navigation, and content-summarization.

2018-11-25

ArXiv (preprint)

Towards Gene Expression Convolutions using Gene Interaction Graphs

We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the… (see more) data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-protein, co-expression, or research paper text association) can be used to impose a bias on a deep model similar to the spatial bias imposed by convolutions on an image. We explore the usage of Graph Convolutional Neural Networks coupled with dropout and gene embeddings to utilize the graph information. We find this approach provides an advantage for particular tasks in a low data regime but is very dependent on the quality of the graph used. We conclude that more work should be done in this direction. We design experiments that show why existing methods fail to capture signal that is present in the data when features are added which clearly isolates the problem that needs to be addressed.

2018-06-18

ArXiv (preprint)

Learning to rank for censored survival data

Margaux Luck

Tristan Sylvain

Heloise Cardinal

Andrea Lodi

Survival analysis is a type of semi-supervised ranking task where the target output (the survival time) is often right-censored. Utilizing t… (see more)his information is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions, namely partial likelihood methods, rank methods, and our classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier estimate of the probability density to impute the labels of censored examples, can take advantage of this information. The proposed method allows us to have a model that predict the probability distribution of an event. If a clinician had access to the detailed probability of an event over time this would help in treatment planning. For example, determining if the risk of kidney graft rejection is constant or peaked after some time. Also, we demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for ranking survival models.

2018-06-06

ArXiv (preprint)

Graph Priors for Deep Neural Networks

In this work we explore how gene-gene interaction graphs can be used as a prior for the representation of a model to construct features base… (see more)d on known interactions between genes. Most existing machine learning work on graphs focuses on building models when data is confined to a graph structure. In this work we focus on using the information from a graph to build better representations in our models. We use the percolate task, determining if a path exists across a grid for a set of node values, as a proxy for gene pathways. We create variants of the percolate task to explore where existing methods fail. We test the limits of existing methods in order to determine what can be improved when applying these methods to a real task. This leads us to propose new methods based on Graph Convolutional Networks (GCN) that use pooling and dropout to deal with noise in the graph prior.

2018-02-12

(published)

openreview.net

BigBrain: 1D convolutional neural networks for automated sementation of cortical layers

Konrad Wagstyl

Claude Lepage

Karl Zilles

Sebastian Bludau

G. Cucurul

Alan C. Evans

Paul C Fletcher

Thomas Funck

Katrin Amunts