Classification and segmentation of kidney MRI images for chronic kidney disease detection

MSB Islam, MSI Sumon, R Sarmun, EH Bhuiyan… - Computers and …, 2024 - Elsevier
Abstract Chronic Kidney Disease (CKD) is a common ailment with significant public health
implications, underscoring the critical importance of early detection and diagnosis for …

[HTML][HTML] A Review of Advancements and Challenges in Liver Segmentation

D Wei, Y Jiang, X Zhou, D Wu, X Feng - Journal of Imaging, 2024 - mdpi.com
Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring,
and surgical planning due to the complex anatomical structure and physiological functions …

Artificial intelligence techniques in liver cancer.

L Wang, M Fatemi, A Alizad - Frontiers in Oncology, 2024 - europepmc.org
Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant
contributor to worldwide cancer-related deaths. Various medical imaging techniques …

Automated grading of prenatal hydronephrosis severity from segmented kidney ultrasounds using deep learning

S Mahmud, TO Abbas, MEH Chowdhury… - Expert Systems with …, 2024 - Elsevier
Background and motivations Antenatal or prenatal hydronephrosis (AHN) is a common
kidney complication in unborn children. While AHN is generally benign and resolves over …

[HTML][HTML] Training robust T1-weighted magnetic resonance imaging liver segmentation models using ensembles of datasets with different contrast protocols and liver …

N Patel, A Celaya, M Eltaher, R Glenn, KB Savannah… - Scientific Reports, 2024 - nature.com
Image segmentation of the liver is an important step in treatment planning for liver cancer.
However, manual segmentation at a large scale is not practical, leading to increasing …

[HTML][HTML] Deep Learning Technology and Image Sensing

SH Lee, DK Kang - Sensors, 2024 - mdpi.com
The scientific landscape is constantly evolving, marked by groundbreaking advancements in
imaging, sensing, and machine learning that expand the realms of possibility across various …

MRSegmentator: Robust Multi-Modality Segmentation of 40 Classes in MRI and CT Sequences

H Häntze, L Xu, FJ Dorfner, L Donle, D Truhn… - arXiv preprint arXiv …, 2024 - arxiv.org
Purpose: To introduce a deep learning model capable of multi-organ segmentation in MRI
scans, offering a solution to the current limitations in MRI analysis due to challenges in …

Non-Intrusive Load State Monitoring Through Smart Meter‎ Disaggregation Using 1d Deep‎ Reconstruction Networks

S Mahmud, M Houchati, F Bensaali… - Available at SSRN … - papers.ssrn.com
Non-intrusive load monitoring (NILM), achieved through the disaggregation of smart meter
load consumption data, offers‎ numerous applications in managing the demand side of smart …