Scientific Research Publishing

UWB NLOS Signal Recognition Based on Deep Learning ()

However, in indoor environments, non-line-of-sight (NLOS) signals significantly degrade the ranging performance of UWB ...
IEEE

Learning Compact Semantic Information and Reliable Pseudo-labels for Incomplete Multi-View Multi-Label Classification

Abstract: Multi-view data encompasses various data types, including multi-feature, multi-sequence, and multi-modal data. Multi-view multi-label classification aims to leverage the rich semantic ...
EurekAlert!

Label attention meets deep learning: a novel model for ICD-11 automatic classification of Chinese EMRs

Electronic medical records (EMRs) enable healthcare institutions to digitally document patients’ clinical conditions, treatment processes, and diagnostic outcomes, supporting paperless clinical ...
Frontiers

VAE deep learning model with domain adaptation, transfer learning and harmonization for diagnostic classification from multi-site neuroimaging data

The acquisition sites include: CALTECH, California Institute of Technology; CMU, Carnegie Mellon University; KKI, Kennedy Krieger Institute; LEUVEN, University of Leuven; MAX, Ludwig Maximilians ...
Scientific Research Publishing

Comparative Research in Sentiment Analysis Using Machine Learning Technique ()

In today’s digital background, sentiment analysis has become an essential factor of Natural Language Processing (NLP), offering valuable insights from vast online data sources. This paper presents a ...
IEEE

Multi-label Chest X-ray Image Classification via Category Disentangled Causal Learning

Abstract: Chest X-rays (CXR) are widely used to diagnose chest diseases. Since patients often suffer from multiple diseases simultaneously, it is crucial to identify multiple abnormalities in a single ...
Frontiers

Deep learning-based classification of multiple fundus diseases using ultra-widefield images

This study aimed to develop a hybrid deep learning model for classifying multiple fundus diseases using ultra-widefield (UWF) images, thereby improving diagnostic efficiency and accuracy while ...
Nature

Active Learning for Multi-Label Classification

Active learning for multi-label classification addresses the challenge of labelling data in situations where each instance may belong to several overlapping categories. This paradigm aims to enhance ...
GitHub

jimsyjohnson/Multi-task-Deep-Learning-for-Automatic-Cancer-Segmentation-and-Classification-in-3D-CT-Scans

This project implements a multi-task nnU-Net v2 pipeline for pancreas and lesion segmentation from 3D CT scans. The model leverages a shared encoder for feature extraction and dual decoders for ...