Zobrazeno 1 - 10
of 43
pro vyhledávání: '"Yoon, Jee Seok"'
Medical image analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. Ho
Externí odkaz:
http://arxiv.org/abs/2310.08598
Publikováno v:
Proceedings of Information Processing in Medical Imaging, 2023, pp. 388-400
Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via im
Externí odkaz:
http://arxiv.org/abs/2212.08228
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g.
Externí odkaz:
http://arxiv.org/abs/2207.13223
Existing studies on disease diagnostic models focus either on diagnostic model learning for performance improvement or on the visual explanation of a trained diagnostic model. We propose a novel learn-explain-reinforce (LEAR) framework that unifies d
Externí odkaz:
http://arxiv.org/abs/2108.09451
There exists an apparent negative correlation between performance and interpretability of deep learning models. In an effort to reduce this negative correlation, we propose a Born Identity Network (BIN), which is a post-hoc approach for producing mul
Externí odkaz:
http://arxiv.org/abs/2011.10381
In recent years, deep learning-based feature representation methods have shown a promising impact in electroencephalography (EEG)-based brain-computer interface (BCI). Nonetheless, owing to high intra- and inter-subject variabilities, many studies on
Externí odkaz:
http://arxiv.org/abs/1910.07747
In this article, we focus on decomposing latent representations in generative adversarial networks or learned feature representations in deep autoencoders into semantically controllable factors in a semisupervised manner, without modifying the origin
Externí odkaz:
http://arxiv.org/abs/1905.11088
Publikováno v:
In NeuroImage June 2023 273
Publikováno v:
In Neural Networks July 2019 115:1-10
Publikováno v:
In Deep Learning for Medical Image Analysis Edition: Second Edition. 2024:3-31