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Found 9 papers, 3 papers with code
Which Augmentation Should I Use? An Empirical Investigation of Augmentations for Self-Supervised Phonocardiogram Representation Learning
Despite the recent increase in research activity, deep-learning models have not yet been widely accepted in several real-world settings, such as medicine.
Multi-Scale and Multi-Layer Contrastive Learning for Domain Generalization
During the past decade, deep neural networks have led to fast-paced progress and significant achievements in computer vision problems, for both academia and industry.
CNN Feature Map Augmentation for Single-Source Domain Generalization
In search of robust and generalizable machine learning models, Domain Generalization (DG) has gained significant traction during the past few years.
CNNs with Multi-Level Attention for Domain Generalization
In the present work, we focus on this problem of Domain Generalization and propose an alternative neural network architecture for robust, out-of-distribution image classification.
Towards Domain Generalization for ECG and EEG Classification: Algorithms and Benchmarks
Our objective in this work is to propose a benchmark for evaluating DG algorithms, in addition to introducing a novel architecture for tackling DG in biosignal classification.
An Artificial Intelligence Outlook for Colorectal Cancer Screening
Colorectal cancer is the third most common tumor in men and the second in women, accounting for 10% of all tumors worldwide.
Listen2YourHeart: A Self-Supervised Approach for Detecting Murmur in Heart-Beat Sounds
The PhysioNet 2022 challenge targets automatic detection of murmur from audio recordings of the heart and automatic detection of normal vs. abnormal clinical outcome.
A Domain Generalization Approach for Out-Of-Distribution 12-lead ECG Classification with Convolutional Neural Networks
Deep Learning systems have achieved great success in the past few years, even surpassing human intelligence in several cases.
Multi-layer Representation Learning for Robust OOD Image Classification
Convolutional Neural Networks have become the norm in image classification.
