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 …

Updated primer on generative artificial intelligence and large language models in medical imaging for medical professionals

K Kim, K Cho, R Jang, S Kyung, S Lee… - Korean Journal of …, 2024 - pmc.ncbi.nlm.nih.gov
The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot
developed by OpenAI, has garnered interest in the application of generative artificial …

Overcoming the challenges in the development and implementation of artificial intelligence in radiology: a comprehensive review of solutions beyond supervised …

GS Hong, M Jang, S Kyung, K Cho… - Korean Journal of …, 2023 - pmc.ncbi.nlm.nih.gov
Artificial intelligence (AI) in radiology is a rapidly develo** field with several prospective
clinical studies demonstrating its benefits in clinical practice. In 2022, the Korean Society of …

Computational modelling in disorders of consciousness: Closing the gap towards personalised models for restoring consciousness

AI Luppi, J Cabral, R Cofre, PAM Mediano, FE Rosas… - NeuroImage, 2023 - Elsevier
Disorders of consciousness are complex conditions characterised by persistent loss of
responsiveness due to brain injury. They present diagnostic challenges and limited options …

Whole MILC: generalizing learned dynamics across tasks, datasets, and populations

U Mahmood, MM Rahman, A Fedorov, N Lewis… - … Image Computing and …, 2020 - Springer
Behavioral changes are the earliest signs of a mental disorder, but arguably, the dynamics
of brain function gets affected even earlier. Subsequently, spatio-temporal structure of …

Ml-based medical image analysis for anomaly detection in CT scans, x-rays, and MRIs

M Siddiq - Devotion: Journal of Research and Community …, 2020 - devotion.greenvest.co.id
The area of medical image analysis is examined in this review article along with its potential
to revolutionize healthcare. The article starts off by going through the different kinds of …

Learnt dynamics generalizes across tasks, datasets, and populations

U Mahmood, MM Rahman, A Fedorov, Z Fu… - arxiv preprint arxiv …, 2019 - arxiv.org
Differentiating multivariate dynamic signals is a difficult learning problem as the feature
space may be large yet often only a few training examples are available. Traditional …

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AI Luppi, SN Ching, MN Diringer - NeuroImage, 2023 - digitalcommons.wustl.edu
abstract Disorders of consciousness are complex conditions characterised by persistent loss
of responsiveness due to brain injury. They present diagnostic challenges and limited …

Representation Learning of FMRI Data Using Variational Autoencoder

JH Kim - 2021 - search.proquest.com
Functional imaging data of the brain using Magnetic Resonance Imaging (MRI)–fMRI data
exhibits complex but structured patterns. This fMRI data has opened a new venue for …

[PDF][PDF] Anomaly Detection in Medical Imaging-A Mini

ME Tschuchnig, M Gadermayr - arxiv preprint arxiv:2108.11986, 2021 - academia.edu
The increasing digitization of medical imaging enables machine learning based
improvements in detecting, visualizing and segmenting lesions, easing the workload for …