Efficient Medical Artificial Intelligence, Kartoniert / Broschiert

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.- 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.