Search Results for author:
Found 10 papers, 7 papers with code
Alzheimer's disease detection in PSG signals
This study delves into the potential of utilizing sleep-related electroencephalography (EEG) signals acquired through polysomnography (PSG) for the early detection of AD.
MIFA: Metadata, Incentives, Formats, and Accessibility guidelines to improve the reuse of AI datasets for bioimage analysis
These include standards on data formats, metadata, data presentation and sharing, and incentives to generate new datasets.
BioImage.IO Chatbot: A Community-Driven AI Assistant for Integrative Computational Bioimaging
We present the BioImage$.$IO Chatbot, an AI assistant powered by Large Language Models and supported by a community-driven knowledge base and toolset.
ABANICCO: A New Color Space for Multi-Label Pixel Classification and Color Segmentation
In any computer vision task involving color images, a necessary step is classifying pixels according to color and segmenting the respective areas.
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the IEEE Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing.
Translational Lung Imaging Analysis Through Disentangled Representations
The development of new treatments often requires clinical trials with translational animal models using (pre)-clinical imaging to characterize inter-species pathological processes.
Deep learning based domain adaptation for mitochondria segmentation on EM volumes
This is a problem known as domain adaptation, since models that learned from a sample distribution (or source domain) struggle to maintain their performance on samples extracted from a different distribution or target domain.
Search for temporal cell segmentation robustness in phase-contrast microscopy videos
Studying cell morphology changes in time is critical to understanding cell migration mechanisms.
Stable deep neural network architectures for mitochondria segmentation on electron microscopy volumes
For that reason, and following a recent code of best practices for reporting experimental results, we present an extensive study of the state-of-the-art deep learning architectures for the segmentation of mitochondria on EM volumes, and evaluate the impact in performance of different variations of 2D and 3D U-Net-like models for this task.
Benchmarking of image registration methods for differently stained histological slides
Image registration is a common task for many biomedical analysis applications.
