2 citations · 2 across the 16 of their papers we have counts for
8 papers · 1 filter
QC-SMOTE: Quality-Controlled SMOTE for Imbalanced Classification
Parth Upman, Shreyank N Gowda
Class imbalance poses a significant challenge in classification, where existing methods such as SMOTE often generate low-quality synthetic samples in regions with noise or class ov…
Using Explainability as a Training-Time Reliability Signal for Efficient ECG Classification
Veerendhra Kumar Dangeti, Xiao Gu, Ying Weng +1
Training deep neural networks for clinical time-series analysis is computationally demanding, yet many healthcare settings lack the resources required for repeated model developmen…
Extending Pretrained 10-Second ECG Foundation Models to Longer Horizons
Wei Tang, Jinpei Han, Kangning Cui +10
Electrocardiogram (ECG) foundation models pretrained on typical diagnostic 10-second ECG segments, have demonstrated strong transferability across a range of clinical applications.…
Adaptive Data Dropout: Towards Self-Regulated Learning in Deep Neural Networks
Amar Gahir, Varshil Patel, Shreyank N Gowda
Deep neural networks are typically trained by uniformly sampling large datasets across epochs, despite evidence that not all samples contribute equally throughout learning. Recent…
Sensing Cardiac Health Across Scenarios and Devices: A Multi-Modal Foundation Model Pretrained on Heterogeneous Data from 1.7 Million Individuals
Xiao Gu, Wei Tang, Jinpei Han +10
Cardiac biosignals, such as electrocardiograms (ECG) and photoplethysmograms (PPG), are of paramount importance for the diagnosis, prevention, and management of cardiovascular dise…
CAPT: Class-Aware Prompt Tuning for Federated Long-Tailed Learning with Vision-Language Model
Shihao Hou, Xinyi Shang, Shreyank N Gowda +4
Effectively handling the co-occurrence of non-IID data and long-tailed distributions remains a critical challenge in federated learning. While fine-tuning vision-language models (V…