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This paper introduces R-U-Net, an ECG delineation model that pairs a ResNet-18 encoder with a U-Net decoder, achieving superior performance in semi-supervised learning settings compared to existing methods.
QFedPolyp proposes a federated learning framework for polyp segmentation that uses quantization-aware training to reduce communication costs and achieve faster inference while preserving privacy.
A tutorial from freeCodeCamp teaching how to build a tumor segmentation pipeline for breast ultrasound images using MONAI, emphasizing data profiling before model selection.
Proposes EA-RMENet, a deep learning model for radio map estimation that achieves high accuracy and efficiency using an EfficientNetB5 encoder, attention gated skip connections, and ASPP.