Efficient Medical Artificial Intelligence

.- Beyond one-hot encoding? Journey into compact encoding for large multi-class segmentation.
.- BrainNetMLP: An Efficient and Effective Baseline for Functional Brain Network Classification.
.- Conquering the Retina: Bringing Visual in-Context Learning to OCT.
.- Electrocardiogram Feature Extraction: A Quantitative Comparison of Signal Reconstruction Using Traditional and Autoencoder Methods.
.- Improving Few-Shot-Segmentation of New Structures in Volumetric Medical Images by Support Set Optimization.
.- Towards Radar-Driven Speech Therapy: Multimodal Training withUltrasound, Audio, and Radar for Unimodal Phonetic Segment Classification.
.- EU-Net: Efficient Training of U-Net for Biomedical Image Segmentation.
.- Multi-Scale Spatial Context with Learnable High-Frequency Augmentation for Polyp Segmentation.
.- EL-UNet: An Efficient and Lightweight U-Net with Multi-Scale Attention for Medical Image Segmentation.
.- SwiM-UNet: A Lightweight Hybrid Swin Transformer-Vision Mamba U-Net with a Novel Adapter Design.
.- Federated and Continual Learning of AI models from Routine Clinical Data Under Privacy Constraints.
.- From O(n2) to O(n) Parameters: Quantum Self-Attention in Vision Transformers for Biomedical Image Classification.
.- Human-in-the-Loop Active Learning for Real-Time Endoscopic Diagnostics on Edge Devices.
.- Triple Expert Adaptation Networks with Adaptive Prompt Selection for Multi-Modal Medical Image Fusion.
.- Towards Efficient and Privacy-Preserving Medical Image Segmentation:A Point-Driven Source-Free Active Adaptation Framework.
.- CSTNet: A Generative Framework for EEG-to-ECoG Mapping via Optimal Transport .
.- Spiking MU-Net: Toward Low-Power and Efficient Microscopic Image Segmentation.
.- SAM 2 in Robotic Surgery: An Empirical Evaluation for Robustness and Generalization in Surgical Video Segmentation.
.- EcoScale-Net: A Lightweight Multi-Kernel Network for Long-Sequence 12-lead ECG Classification.
.- Impact of Clinical Image Quality on Efficient Foundation Model Finetuning.
.- Continual Multiple Instance Learning for Hematologic Disease Diagnosis.
.- MIP-Based Tumor Segmentation: A Radiologist-Inspired Approach.
.- Interpretability-Aware Pruning for Efficient Medical Image Analysis.
.- PeekNet: A Power and Efficiency-Enhanced Knowledge-Aware Network for Real-Time Capsule Endoscopy Image Classification.
.- Efficient Foundation Model Pre-training on Mixed Retina Images from Similar Modalities.
.- niiv: Interactive Self-supervised Neural Implicit Isotropic Volume Reconstruction.
.- RARE-UNet: Resolution-Aligned Routing Entry for Adaptive Medical Image Segmentation.
.- Graph-based LLM over Semi-Structured Population Data for Dynamic Policy Response.
.- Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells.
.- EndoSfM3D: Learning to 3D Reconstruct Any Endoscopic Surgery Scene using Self-supervised Foundation Model .
.- WeakSupCon: Weakly Supervised Contrastive Learning for Encoder Pre-training.
.- DetSAM: A Joint Detection-and-Segmentation Learning Framework for Multi-Class Surgical Instrument Segmentation.
.- Debunking Optimization Myths in Federated Learning for Medical Image Classification.
.- A Staining Variability-Aware Semi-Supervised Framework for H&E-to-IHC Virtual Staining.
.- Counterfactual Augmentation for Long-Tailed Multi-Label Chest X-ray Classification.
.- Domain-Incremental Continual Learning for Robust Surgical Tool Segmentation.