Francis Dutil

Joseph Paul Cohen

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only … (voir plus)in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this assumption is correct. In some prediction tasks, such as for medical images, one may have some images with masks drawn by a human expert, indicating a region of the image containing relevant information to make the prediction. We study multiple methods that take advantage of such auxiliary labels, by training networks to ignore distracting features which may be found outside of the region of interest. This mask information is only used during training and has an impact on generalization accuracy depending on the severity of the shift between the training and test distributions. Surprisingly, while these methods improve generalization performance in the presence of a covariate shift, there is no strong correspondence between the correction of attribution towards the features a human expert have labelled as important and generalization performance. These results suggest that the root cause of poor generalization may not always be spatially defined, and raise questions about the utility of masks as 'attribution priors' as well as saliency maps for explainable predictions.

2021-01-01

ICLR (publié)

openreview.net

Cross-Modal Information Maximization for Medical Imaging: CMIM

Tristan Sylvain

Tess Berthier

Lisa Di Jorio

Margaux Luck

(Rex) Devon Hjelm

2020-10-20

ArXiv (prépublication)

InfoMask: Masked Variational Latent Representation to Localize Chest Disease

Saeid Asgari Taghanaki

Mohammad Havaei

Tess Berthier

Lisa Di Jorio

Ghassan Hamarneh

2019-10-10

Lecture Notes in Computer Science (publié)

doi.org

GradMask: Reduce Overfitting by Regularizing Saliency

Becks Simpson

Joseph Paul Cohen

With too few samples or too many model parameters, overfitting can inhibit the ability to generalise predictions to new data. Within medical… (voir plus) 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 (inconnu)

openreview.net

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… (voir plus) 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 (prépublication)

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… (voir plus)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

(publié)

openreview.net

Adversarial Generation of Natural Language

Sai Rajeswar

Generative Adversarial Networks (GANs) have gathered a lot of attention from the computer vision community, yielding impressive results for … (voir plus)image generation. Advances in the adversarial generation of natural language from noise however are not commensurate with the progress made in generating images, and still lag far behind likelihood based methods. In this paper, we take a step towards generating natural language with a GAN objective alone. We introduce a simple baseline that addresses the discrete output space problem without relying on gradient estimators and show that it is able to achieve state-of-the-art results on a Chinese poem generation dataset. We present quantitative results on generating sentences from context-free and probabilistic context-free grammars, and qualitative language modeling results. A conditional version is also described that can generate sequences conditioned on sentence characteristics.

2017-08-01

Proceedings of the 2nd Workshop on Representation Learning for NLP (publié)

doi.org