Evaluation of pseudo-healthy image reconstruction for anomaly detection with deep generative models: Application to brain FDG PET
Over the past years, pseudo-healthy reconstruction for unsupervised anomaly detection has
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
gained in popularity. This approach has the great advantage of not requiring tedious pixel …
An overview on deep clustering
X Wei, Z Zhang, H Huang, Y Zhou - Neurocomputing, 2024 - Elsevier
In recent years, with the great success of deep learning and especially deep unsupervised
learning, many deep architectural clustering methods, collectively known as deep clustering …
learning, many deep architectural clustering methods, collectively known as deep clustering …
Deep generative modeling-based data augmentation with demonstration using the BFBT benchmark void fraction datasets
F Alsafadi, X Wu - Nuclear Engineering and Design, 2023 - Elsevier
Deep learning (DL) has achieved remarkable successes in many disciplines such as
computer vision and natural language processing due to the availability of “big data” …
computer vision and natural language processing due to the availability of “big data” …
Kullback-Leibler Divergence Based Regularized Normalization for Low Resource Tasks
Large pretrained models like Bert, GPT, and Wav2Vec have demonstrated their ability to
learn transferable representations for various downstream tasks. However, obtaining a …
learn transferable representations for various downstream tasks. However, obtaining a …
Unsupervised classification of multi-contrast magnetic resonance histology of peripheral arterial disease lesions using a convolutional variational autoencoder with a …
Purpose To investigate the feasibility of a deep learning algorithm combining variational
autoencoder (VAE) and two-dimensional (2D) convolutional neural networks (CNN) for …
autoencoder (VAE) and two-dimensional (2D) convolutional neural networks (CNN) for …
Optimizing anomaly detection in 3D MRI scans: The role of ConvLSTM in medical image analysis
A Durairaj, ES Madhan, M Rajkumar… - Applied Soft Computing, 2024 - Elsevier
Abstract The analysis of Medical Images (MI), particularly the detection and classification of
anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely …
anomalies in 3D MRI (Magnetic Resonance Imaging) scans, plays a critical part in timely …
[HTML][HTML] Automatic Classification of Magnetic Resonance Histology of Peripheral Arterial Chronic Total Occlusions Using a Variational Autoencoder: A Feasibility …
The novel approach of our study consists in adapting and in evaluating a custom-made
variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks …
variational autoencoder (VAE) using two-dimensional (2D) convolutional neural networks …
PULASki: Learning inter-rater variability using statistical distances to improve probabilistic segmentation
In the domain of medical imaging, many supervised learning based methods for
segmentation face several challenges such as high variability in annotations from multiple …
segmentation face several challenges such as high variability in annotations from multiple …
Pseudo-healthy image reconstruction with variational autoencoders for anomaly detection: A benchmark on 3D brain FDG PET
Many deep generative models have been proposed to reconstruct pseudo-healthy images
for anomaly detection. Among these models, the variational autoencoder (VAE) has …
for anomaly detection. Among these models, the variational autoencoder (VAE) has …
[PDF][PDF] 基于机器学习算法预测核磁共振T 2 谱
张哲， 廖广志， 肖立志， 崔云江， 王培春， 李志愿 - 科学技术与工程, 2023 - stae.com.cn
摘要近年来, 深度学习算法被广泛应用于生成各种类型的数据. 通过分析测井数据与核磁共振T2
谱之间的映射关系, 利用随机森林与长短期记忆(long short-term memory, LSTM) …
谱之间的映射关系, 利用随机森林与长短期记忆(long short-term memory, LSTM) …