Recent advances and clinical applications of deep learning in medical image analysis

X Chen, X Wang, K Zhang, KM Fung, TC Thai… - Medical image …, 2022 - Elsevier
Deep learning has received extensive research interest in developing new medical image
processing algorithms, and deep learning based models have been remarkably successful …

A survey on deep learning applied to medical images: from simple artificial neural networks to generative models

P Celard, EL Iglesias, JM Sorribes-Fdez… - Neural Computing and …, 2023 - Springer
Deep learning techniques, in particular generative models, have taken on great importance
in medical image analysis. This paper surveys fundamental deep learning concepts related …

Emotion recognition in EEG signals using deep learning methods: A review

M Jafari, A Shoeibi, M Khodatars… - Computers in Biology …, 2023 - Elsevier
Emotions are a critical aspect of daily life and serve a crucial role in human decision-making,
planning, reasoning, and other mental states. As a result, they are considered a significant …

Fast unsupervised brain anomaly detection and segmentation with diffusion models

WHL Pinaya, MS Graham, R Gray, PF Da Costa… - … Conference on Medical …, 2022 - Springer
Deep generative models have emerged as promising tools for detecting arbitrary anomalies
in data, dispensing with the necessity for manual labelling. Recently, autoregressive …

Label-free liver tumor segmentation

Q Hu, Y Chen, J Xiao, S Sun, J Chen… - Proceedings of the …, 2023 - openaccess.thecvf.com
We demonstrate that AI models can accurately segment liver tumors without the need for
manual annotation by using synthetic tumors in CT scans. Our synthetic tumors have two …

What is machine learning, artificial neural networks and deep learning?—Examples of practical applications in medicine

J Kufel, K Bargieł-Łączek, S Kocot, M Koźlik… - Diagnostics, 2023 - mdpi.com
Machine learning (ML), artificial neural networks (ANNs), and deep learning (DL) are all
topics that fall under the heading of artificial intelligence (AI) and have gained popularity in …

Segment any anomaly without training via hybrid prompt regularization

Y Cao, X Xu, C Sun, Y Cheng, Z Du, L Gao… - arXiv preprint arXiv …, 2023 - arxiv.org
We present a novel framework, ie, Segment Any Anomaly+(SAA+), for zero-shot anomaly
segmentation with hybrid prompt regularization to improve the adaptability of modern …

Label-free segmentation of COVID-19 lesions in lung CT

Q Yao, L Xiao, P Liu, SK Zhou - IEEE transactions on medical …, 2021 - ieeexplore.ieee.org
Scarcity of annotated images hampers the building of automated solution for reliable COVID-
19 diagnosis and evaluation from CT. To alleviate the burden of data annotation, we herein …

Segmentmeifyoucan: A benchmark for anomaly segmentation

R Chan, K Lis, S Uhlemeyer, H Blum, S Honari… - arXiv preprint arXiv …, 2021 - arxiv.org
State-of-the-art semantic or instance segmentation deep neural networks (DNNs) are
usually trained on a closed set of semantic classes. As such, they are ill-equipped to handle …

Anomaly detection in medical imaging-a mini review

ME Tschuchnig, M Gadermayr - International Data Science Conference, 2021 - Springer
The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …