no code implementations • 5 Oct 2023 • Jee Seok Yoon, Kwanseok Oh, Yooseung Shin, Maciej A. Mazurowski, Heung-Il Suk
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.
1 code implementation • 16 Dec 2022 • Jee Seok Yoon, Chenghao Zhang, Heung-Il Suk, Jia Guo, Xiaoxiao Li
To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images.
1 code implementation • 27 Jul 2022 • Ahmad Wisnu Mulyadi, Wonsik Jung, Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
By considering this pseudo map as an enriched reference, we employ an estimating network to estimate the AD likelihood map over a 3D sMRI scan.
1 code implementation • 21 Aug 2021 • Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
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.
1 code implementation • 20 Nov 2020 • Kwanseok Oh, Jee Seok Yoon, Heung-Il Suk
Specifically, our proposed BIN consists of two core components: Counterfactual Map Generator and Target Attribution Network.
1 code implementation • 17 Oct 2019 • Eunjin Jeon, Wonjun Ko, Jee Seok Yoon, Heung-Il Suk
In this paper, we propose a novel framework that learns class-relevant and subject-invariant feature representations in an information-theoretic manner, without using adversarial learning.
2 code implementations • 27 May 2019 • Jee Seok Yoon, Myung-Cheol Roh, Heung-Il Suk
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 original trained models.