research
∙
04/02/2023
The Effect of Counterfactuals on Reading Chest X-rays
This study evaluates the effect of counterfactual explanations on the in...
share
research
∙
12/27/2021
Multi-Domain Balanced Sampling Improves Out-of-Distribution Generalization of Chest X-ray Pathology Prediction Models
Learning models that generalize under different distribution shifts in m...
research
∙
10/31/2021
TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with ches...
research
∙
02/18/2021
Benefits of Linear Conditioning for Segmentation using Metadata
Medical images are often accompanied by metadata describing the image (v...
research
∙
02/18/2021
Gifsplanation via Latent Shift: A Simple Autoencoder Approach to Progressive Exaggeration on Chest X-rays
Motivation: Traditional image attribution methods struggle to satisfacto...
research
∙
10/20/2020
ivadomed: A Medical Imaging Deep Learning Toolbox
ivadomed is an open-source Python package for designing, end-to-end trai...
research
∙
09/17/2020
S2SD: Simultaneous Similarity-based Self-Distillation for Deep Metric Learning
Deep Metric Learning (DML) provides a crucial tool for visual similarity...
research
∙
07/26/2020
Uniformizing Techniques to Process CT scans with 3D CNNs for Tuberculosis Prediction
A common approach to medical image analysis on volumetric data uses deep...
research
∙
07/08/2020
A Benchmark of Medical Out of Distribution Detection
There is a rise in the use of deep learning for automated medical diagno...
research
∙
06/22/2020
COVID-19 Image Data Collection: Prospective Predictions Are the Future
Across the world's coronavirus disease 2019 (COVID-19) hot spots, the ne...
research
∙
05/24/2020
Predicting COVID-19 Pneumonia Severity on Chest X-ray with Deep Learning
The need to streamline patient management for COVID-19 has become more p...
research
∙
04/28/2020
DiVA: Diverse Visual Feature Aggregation forDeep Metric Learning
Visual Similarity plays an important role in many computer vision applic...
research
∙
04/28/2020
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning
Visual Similarity plays an important role in many computer vision applic...
research
∙
03/25/2020
COVID-19 Image Data Collection
This paper describes the initial COVID-19 open image data collection. It...
research
∙
03/09/2020
Spine intervertebral disc labeling using a fully convolutional redundant counting model
Labeling intervertebral discs is relevant as it notably enables clinicia...
research
∙
03/09/2020
Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?
Despite recent improvements in medical image segmentation, the ability t...
research
∙
02/19/2020
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning
Deep Metric Learning (DML) is arguably one of the most influential lines...
research
∙
02/07/2020
Quantifying the Value of Lateral Views in Deep Learning for Chest X-rays
Most deep learning models in chest X-ray prediction utilize the posteroa...
research
∙
02/06/2020
On the limits of cross-domain generalization in automated X-ray prediction
This large scale study focuses on quantifying what X-rays diagnostic pre...
research
∙
10/21/2019
Is graph biased feature selection of genes better than random?
Gene interaction graphs aim to capture various relationships between gen...
research
∙
10/21/2019
Icentia11K: An Unsupervised Representation Learning Dataset for Arrhythmia Subtype Discovery
We release the largest public ECG dataset of continuous raw signals for ...
research
∙
10/18/2019
The TCGA Meta-Dataset Clinical Benchmark
Machine learning is bringing a paradigm shift to healthcare by changing ...
research
∙
10/16/2019
Deep Semantic Segmentation of Natural and Medical Images: A Review
The (medical) image semantic segmentation task consists of classifying e...
research
∙
10/01/2019
Underwhelming Generalization Improvements From Controlling Feature Attribution
Overfitting is a common issue in machine learning, which can arise when ...
research
∙
09/14/2019
Torchmeta: A Meta-Learning library for PyTorch
The constant introduction of standardized benchmarks in the literature h...
research
∙
05/06/2019
Analysis of Gene Interaction Graphs for Biasing Machine Learning Models
Gene interaction graphs aim to capture various relationships between gen...
research
∙
04/17/2019
Do Lateral Views Help Automated Chest X-ray Predictions?
Most convolutional neural networks in chest radiology use only the front...
research
∙
04/16/2019
GradMask: Reduce Overfitting by Regularizing Saliency
With too few samples or too many model parameters, overfitting can inhib...
research
∙
01/31/2019
Chester: A Web Delivered Locally Computed Chest X-Ray Disease Prediction System
Deep learning has shown promise to augment radiologists and improve the ...
research
∙
10/08/2018
Towards the Latent Transcriptome
In this work we propose a method to compute continuous embeddings for km...
research
∙
06/18/2018
Towards Gene Expression Convolutions using Gene Interaction Graphs
We study the challenges of applying deep learning to gene expression dat...
research
∙
06/06/2018
Learning to rank for censored survival data
Survival analysis is a type of semi-supervised ranking task where the ta...
research
∙
05/22/2018
Distribution Matching Losses Can Hallucinate Features in Medical Image Translation
This paper discusses how distribution matching losses, such as those use...
research
∙
12/12/2017
GibbsNet: Iterative Adversarial Inference for Deep Graphical Models
Directed latent variable models that formulate the joint distribution as...
research
∙
07/20/2017
ShortScience.org - Reproducing Intuition
We present ShortScience.org, a platform for post-publication discussion ...
research
∙
03/25/2017
Count-ception: Counting by Fully Convolutional Redundant Counting
Counting objects in digital images is a process that should be replaced ...
research
∙
03/14/2016
Rapid building detection using machine learning
This work describes algorithms for performing discrete object detection,...
research
∙
02/18/2016
RandomOut: Using a convolutional gradient norm to rescue convolutional filters
Filters in convolutional neural networks are sensitive to their initiali...
research
∙
01/05/2016