1 code implementation • 28 Oct 2023 • Hao Wang, Euijoon Ahn, Lei Bi, Jinman Kim
The clinical diagnosis of skin lesion involves the analysis of dermoscopic and clinical modalities.
no code implementations • 20 Mar 2023 • Hao Wang, Euijoon Ahn, Jinman Kim
These SSL approaches, however, are not designed for handling multi-resolution WSIs, which limits their performance in learning discriminative image features.
no code implementations • 10 Feb 2023 • Wei-Chien Wang, Euijoon Ahn, Dagan Feng, Jinman Kim
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks.
no code implementations • 4 Jan 2023 • Yusheng Zhou, Hao Li, Jianan Liu, Zhengmin Kong, Tao Huang, Euijoon Ahn, Zhihan Lv, Jinman Kim, David Dagan Feng
Our results substantiate the potential of UNAEN as a promising solution applicable in real-world clinical environments, with the capability to enhance diagnostic accuracy and facilitate image-guided therapies.
no code implementations • 12 Dec 2022 • Yuan Yuan, Euijoon Ahn, Dagan Feng, Mohamad Khadra, Jinman Kim
However, existing state of the art AI algorithms which are based on deep learning technology are often limited to 2D images that fails to capture inter-slice correlations in 3D volumetric images.
no code implementations • 13 May 2022 • Jianan Liu, Hao Li, Tao Huang, Euijoon Ahn, Kang Han, Adeel Razi, Wei Xiang, Jinman Kim, David Dagan Feng
However, the difference in degradation representations between synthetic and authentic LR images suppresses the quality of SR images reconstructed from authentic LR images.
1 code implementation • 16 Jul 2021 • Hao Wang, Euijoon Ahn, Jinman Kim
To address these problems, we present a novel self-supervised spatiotemporal learning framework for remote physiological signal representation learning, where there is a lack of labelled training data.
1 code implementation • 11 Jul 2021 • Euijoon Ahn, Dagan Feng, Jinman Kim
Hence, we propose a new spatial guided self-supervised clustering network (SGSCN) for medical image segmentation, where we introduce multiple loss functions designed to aid in grouping image pixels that are spatially connected and have similar feature representations.
1 code implementation • CVPR 2020 • Yuyu Guo, Lei Bi, Euijoon Ahn, Dagan Feng, Qian Wang, Jinman Kim
SVIN introduces dual networks: first is the spatiotemporal motion network that leverages the 3D convolutional neural network (CNN) for unsupervised parametric volumetric registration to derive spatiotemporal motion field from two-image volumes; the second is the sequential volumetric interpolation network, which uses the derived motion field to interpolate image volumes, together with a new regression-based module to characterize the periodic motion cycles in functional organ structures.
no code implementations • 7 Jun 2019 • Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim
Hence, we propose a new unsupervised feature learning method that learns feature representations to then differentiate dissimilar medical images using an ensemble of different convolutional neural networks (CNNs) and K-means clustering.
no code implementations • 15 Mar 2019 • Euijoon Ahn, Ashnil Kumar, Dagan Feng, Michael Fulham, Jinman Kim
The accuracy and robustness of image classification with supervised deep learning are dependent on the availability of large-scale, annotated training data.
no code implementations • 16 Jul 2018 • Euijoon Ahn, Jinman Kim, Ashnil Kumar, Michael Fulham, Dagan Feng
The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems.
no code implementations • 12 Mar 2017 • Lei Bi, Jinman Kim, Euijoon Ahn, Dagan Feng
Dermoscopy images play an important role in the non-invasive early detection of melanoma [1].