Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 56,851 to 56,860 of 227,153 articles

About an Automating Annotation Method for Robot Markers

arXiv
Factory automation has become increasingly important due to labor shortages, leading to the introduction of autonomous mobile robots for tasks such as material transportation. Markers are commonly used for robot self-localization and object identific... read more 

Leveraging Multi-Rater Annotations to Calibrate Object Detectors in Microscopy Imaging

arXiv
Deep learning-based object detectors have achieved impressive performance in microscopy imaging, yet their confidence estimates often lack calibration, limiting their reliability for biomedical applications. In this work, we introduce a new approach ... read more 

Leveraging Convolutional Sparse Autoencoders for Robust Movement Classification from Low-Density sEMG

arXiv
Reliable control of myoelectric prostheses is often hindered by high inter-subject variability and the clinical impracticality of high-density sensor arrays. This study proposes a deep learning framework for accurate gesture recognition using only tw... read more 

Scale Equivariance Regularization and Feature Lifting in High Dynamic Range Modulo Imaging

arXiv
Modulo imaging enables high dynamic range (HDR) acquisition by cyclically wrapping saturated intensities, but accurate reconstruction remains challenging due to ambiguities between natural image edges and artificial wrap discontinuities. This work pr... read more 

HierLoc: Hyperbolic Entity Embeddings for Hierarchical Visual Geolocation

arXiv
Visual geolocalization, the task of predicting where an image was taken, remains challenging due to global scale, visual ambiguity, and the inherently hierarchical structure of geography. Existing paradigms rely on either large-scale retrieval, which... read more 

Vision-Language Controlled Deep Unfolding for Joint Medical Image Restoration and Segmentation

arXiv
We propose VL-DUN, a principled framework for joint All-in-One Medical Image Restoration and Segmentation (AiOMIRS) that bridges the gap between low-level signal recovery and high-level semantic understanding. While standard pipelines treat these tas... read more 

To See Far, Look Close: Evolutionary Forecasting for Long-term Time Series

arXiv
The prevailing Direct Forecasting (DF) paradigm dominates Long-term Time Series Forecasting (LTSF) by forcing models to predict the entire future horizon in a single forward pass. While efficient, this rigid coupling of output and evaluation horizons... read more 

Regularisation in neural networks: a survey and empirical analysis of approaches

arXiv
Despite huge successes on a wide range of tasks, neural networks are known to sometimes struggle to generalise to unseen data. Many approaches have been proposed over the years to promote the generalisation ability of neural networks, collectively kn... read more 

Compressed BC-LISTA via Low-Rank Convolutional Decomposition

arXiv
We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed {forward and backward} operators that preserve reconstruction accuracy. We propose a Compressed Block-Convolutional (C-BC) measurement model based on a low-rank Co... read more 

On Safer Reinforcement Learning Policies for Sedation and Analgesia in Intensive Care

arXiv
Pain management in intensive care usually involves complex trade-offs between therapeutic goals and patient safety, since both inadequate and excessive treatment may induce serious sequelae. Reinforcement learning can help address this challenge by l... read more