59 citations · 222 across the 12 of their papers we have counts for
13 papers
Design, Development, and Evaluation of an Interactive Personalized Social Robot to Monitor and Coach Post-Stroke Rehabilitation Exercises
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic +2
Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However,…
ExplainFix: Explainable Spatially Fixed Deep Networks
Alex Gaudio, Christos Faloutsos, Asim Smailagic +2
Is there an initialization for deep networks that requires no learning? ExplainFix adopts two design principles: the "fixed filters" principle that all spatial filter weights of co…
HeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
Elvin Johnson, Shreshta Mohan, Alex Gaudio +3
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We fra…
Enabling AI and Robotic Coaches for Physical Rehabilitation Therapy: Iterative Design and Evaluation with Therapists and Post-Stroke Survivors
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic +2
Artificial intelligence (AI) and robotic coaches promise the improved engagement of patients on rehabilitation exercises through social interaction. While previous work explored th…
Enhancement of Retinal Fundus Images via Pixel Color Amplification
Alex Gaudio, Asim Smailagic, Aurélio Campilho
We propose a pixel color amplification theory and family of enhancement methods to facilitate segmentation tasks on retinal images. Our novel re-interpretation of the image distort…
Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning
Qiqi Xiao, Jiaxu Zou, Muqiao Yang +5
Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can…
Learned Pre-Processing for Automatic Diabetic Retinopathy Detection on Eye Fundus Images
Asim Smailagic, Anupma Sharan, Pedro Costa +3
Diabetic Retinopathy is the leading cause of blindness in the working-age population of the world. The main aim of this paper is to improve the accuracy of Diabetic Retinopathy det…
Designing Personalized Interaction of a Socially Assistive Robot for Stroke Rehabilitation Therapy
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic +2
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
Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic +2
Rehabilitation assessment is critical to determine an adequate intervention for a patient. However, the current practices of assessment mainly rely on therapist's experience, and a…
O-MedAL: Online Active Deep Learning for Medical Image Analysis
Asim Smailagic, Pedro Costa, Alex Gaudio +9
Active Learning methods create an optimized labeled training set from unlabeled data. We introduce a novel Online Active Deep Learning method for Medical Image Analysis. We extend…
Attention Filtering for Multi-person Spatiotemporal Action Detection on Deep Two-Stream CNN Architectures
João Antunes, Pedro Abreu, Alexandre Bernardino +2
Action detection and recognition tasks have been the target of much focus in the computer vision community due to their many applications, namely, security, robotics and recommenda…
Weighted Multisource Tradaboost
João Antunes, Alexandre Bernardino, Asim Smailagic +1
In this paper we propose an improved method for transfer learning that takes into account the balance between target and source data. This method builds on the state-of-the-art Mul…
MedAL: Deep Active Learning Sampling Method for Medical Image Analysis
Asim Smailagic, Hae Young Noh, Pedro Costa +6
Deep learning models have been successfully used in medical image analysis problems but they require a large amount of labeled images to obtain good performance.Deep learning model…