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Found 12 papers, 5 papers with code
ExplainFix: Explainable Spatially Fixed Deep Networks
We are first to demonstrate that all spatial filters in state-of-the-art convolutional deep networks can be fixed at initialization, not learned.
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
HeartSpot priors are ante-hoc explainable and give a human-interpretable image of the preserved spatial features that clearly outlines the heart.
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction.
Enhancement of Retinal Fundus Images via Pixel Color Amplification
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images.
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults.
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world.
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
The research of a socially assistive robot has a potential to augment and assist physical therapy sessions for patients with neurological and musculoskeletal problems (e. g. stroke).
Opportunities of a Machine Learning-based Decision Support System for Stroke Rehabilitation Assessment
Rehabilitation assessment is critical to determine an adequate intervention for a patient.
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Our online method enhances performance of its underlying baseline deep network.
Attention Filtering for Multi-person Spatiotemporal Action Detection on Deep Two-Stream CNN Architectures
Our method, using fovea attention filtering and our generalized binary loss, achieves a relative video mAP improvement of 20% over the two-stream baseline in AVA, and is competitive with the state-of-the-art in the UCF101-24.
Weighted Multisource Tradaboost
In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data.
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Active learning techniques can be used to minimize the number of required training labels while maximizing the model's performance. In this work, we propose a novel sampling method that queries the unlabeled examples that maximize the average distance to all training set examples in a learned feature space.
