Results 51 to 60 of about 3,440,683 (277)
Multiple-Feature Kernel-Based Probabilistic Clustering for Unsupervised Band Selection [PDF]
This paper presents a new method to perform unsupervised band selection (UBS) with hyperspectral data. The method provides a probabilistic clustering approach. The band images are clustered in the image space by computing their posterior class probability.
Marco Bevilacqua, Yannick Berthoumieu
openaire +2 more sources
Data‐Driven SuStaIn Model of Disability Progression in Amyotrophic Lateral Sclerosis
ABSTRACT Objective To determine whether ordinal Subtype and Stage Inference (SuStaIn) applied to routine ALSFRS‐R item scores can identify reproducible disability progression patterns in amyotrophic lateral sclerosis (ALS) and provide clinically meaningful staging.
Giammarco Milella +5 more
wiley +1 more source
Restoration and Calibration of Tilting Hyperspectral Super-Resolution Image
Tilting sampling is a novel sampling mode for achieving a higher resolution of hyperspectral imagery. However, most studies on the tilting image have only focused on a single band, which loses the features of hyperspectral imagery.
Xizhen Zhang +4 more
doaj +1 more source
Mutual Information-Driven Feature Reduction for Hyperspectral Image Classification
A hyperspectral image (HSI), which contains a number of contiguous and narrow spectral wavelength bands, is a valuable source of data for ground cover examinations.
Md Rashedul Islam +3 more
doaj +1 more source
Systemic sclerosis (SSc) is a rare autoimmune disease defined by immune dysregulation, vasculopathy, and progressive fibrosis of the skin and internal organs. Despite advances in care, major complications such as interstitial lung disease (ILD) and myocardial involvement remain the leading causes of morbidity and mortality.
Cristiana Sieiro Santos +2 more
wiley +1 more source
Unsupervised band selection (UBS), which reduces dimensionality without relying on costly labeled data, is pivotal for hyperspectral image (HSI) analysis.
Ping Ma +6 more
doaj +1 more source
A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Efficient recovery from traumatic or degenerative diseases is a great challenge, even after all the advancements in bone and cartilage regeneration. Machine learning (ML) algorithms have presented opportunities to enhance these aspects by accurately analyzing imaging data.
Maryam Kamaei +9 more
wiley +1 more source
This study investigates the integration of Geospatial Foundation Models (GFMs) into an unsupervised change detection workflow for landslide identification.
Julia Anna Leonardi +3 more
doaj +1 more source
We developed a patient‐derived, functional microfluidic model of the diffuse midline glioma (DMG) blood–brain–tumor barrier (BBTB) comprised of endothelial cells, astrocytes, pericytes, and tumor cells. The system forms perfusable microvasculature, reveals the BBTB retains vascular integrity, identifies DMG‐specific transcriptomic changes distinct from
Kimberly R. Bennett +7 more
wiley +1 more source

