no code implementations • 12 Feb 2024 • Kimberly Helm, Tejas Sudharshan Mathai, Boah Kim, Pritam Mukherjee, Jianfei Liu, Ronald M. Summers
In order to reduce clinician oversight and ensure the validity of the DICOM headers, we propose an automated method to classify the 3D MRI sequence acquired at the levels of the chest, abdomen, and pelvis.
no code implementations • 11 Dec 2023 • Yan Zhuang, Benjamin Hou, Tejas Sudharshan Mathai, Pritam Mukherjee, Boah Kim, Ronald M. Summers
As a new emerging and promising type of generative models, diffusion models have proven to outperform Generative Adversarial Networks (GANs) in multiple tasks, including image synthesis.
no code implementations • 31 Jul 2023 • Boah Kim, Yujin Oh, Bradford J. Wood, Ronald M. Summers, Jong Chul Ye
Blood vessel segmentation in medical imaging is one of the essential steps for vascular disease diagnosis and interventional planning in a broad spectrum of clinical scenarios in image-based medicine and interventional medicine.
no code implementations • 29 Sep 2022 • Boah Kim, Yujin Oh, Jong Chul Ye
Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information.
1 code implementation • 27 Jun 2022 • Boah Kim, Jong Chul Ye
Our proposed DDM is composed of the diffusion and the deformation modules so that DDM can learn spatial deformation information between the source and target volumes and provide a latent code for generating intermediate frames along a geodesic path.
no code implementations • 9 Dec 2021 • Boah Kim, Inhwa Han, Jong Chul Ye
Specifically, the deformation fields are generated by the conditional score function of the deformation between the moving and fixed images, so that the registration can be performed from continuous deformation by simply scaling the latent feature of the score.
no code implementations • NeurIPS 2021 • Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
no code implementations • 2 Nov 2021 • Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
1 code implementation • 29 Sep 2021 • Boah Kim, Jeongsol Kim, Jong Chul Ye
Inspired by the recent success of Vision Transformer (ViT), here we present a new distributed learning framework for image processing applications, allowing clients to learn multiple tasks with their private data.
no code implementations • NeurIPS 2021 • Sangjoon Park, Gwanghyun Kim, Jeongsol Kim, Boah Kim, Jong Chul Ye
For example, this enables neural network training for COVID-19 diagnosis on chest X-ray (CXR) images without collecting patient CXR data across multiple hospitals.
no code implementations • 13 Aug 2020 • Boah Kim, Dong Hwan Kim, Seong Ho Park, Jieun Kim, June-Goo Lee, Jong Chul Ye
However, the existing deep learning methods still have limitation in the preservation of original topology during the deformation with registration vector fields.
no code implementations • 2 Jul 2019 • Boah Kim, Jieun Kim, June-Goo Lee, Dong Hwan Kim, Seong Ho Park, Jong Chul Ye
Medical image registration is one of the key processing steps for biomedical image analysis such as cancer diagnosis.
2 code implementations • 5 Apr 2019 • Boah Kim, Jong Chul Ye
This loss function is based on the observation that the softmax layer of deep neural networks has striking similarity to the characteristic function in the Mumford-Shah functional.