Taejoon Eo
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Author Swipe
View article: Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images
Multimodal AI model for preoperative prediction of axillary lymph node metastasis in breast cancer using whole slide images Open
In breast cancer management, predicting axillary lymph node (ALN) metastasis using whole-slide images (WSIs) of primary tumor biopsies is a challenging and underexplored task for pathologists. We developed METACANS, an multimodal artificia…
View article: Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study
Deep Learning-Based Joint Effusion Classification in Adult Knee Radiographs: A Multi-Center Prospective Study Open
Knee effusion, a common and important indicator of joint diseases such as osteoarthritis, is typically more discernible on magnetic resonance imaging (MRI) scans compared to radiographs. However, the use of radiographs for the early detect…
View article: SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation
SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation Open
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance in fully supervised manner. However, acquiring pixel-level expert annotations is extremely expensive and laborious in medical …
View article: Deep computational microscopy via physics-informed end-to-end learning with a learned forward model
Deep computational microscopy via physics-informed end-to-end learning with a learned forward model Open
Computational microscopy, which merges cutting-edge optical methods with intricate algorithms, offers significant potential for applications such as resolution improvement and quantitative phase retrieval. However, it faces challenges due …
View article: Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering
Small Bowel Detection for Wireless Capsule Endoscopy Using Convolutional Neural Networks with Temporal Filtering Open
By automatically classifying the stomach, small bowel, and colon, the reading time of the wireless capsule endoscopy (WCE) can be reduced. In addition, it is an essential first preprocessing step to localize the small bowel in order to app…
View article: COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training
COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training Open
Recent advances in deep learning-based medical image segmentation studies achieve nearly human-level performance when in fully supervised condition. However, acquiring pixel-level expert annotations is extremely expensive and laborious in …
View article: Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation
Self-Training Based Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma and Cochlea Segmentation Open
With the advances of deep learning, many medical image segmentation studies achieve human-level performance when in fully supervised condition. However, it is extremely expensive to acquire annotation on every data in medical fields, espec…
View article: The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging
The Latest Trends in Attention Mechanisms and Their Application in Medical Imaging Open
Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can es…
View article: No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features
No-reference Automatic Quality Assessment for Colorfulness-Adjusted, Contrast-Adjusted, and Sharpness-Adjusted Images Using High-Dynamic-Range-Derived Features Open
Image adjustment methods are one of the most widely used post-processing techniques for enhancing image quality and improving the visual preference of the human visual system (HVS). However, the assessment of the adjusted images has been m…
View article: <scp>KIKI</scp>‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images
<span>KIKI</span>‐net: cross‐domain convolutional neural networks for reconstructing undersampled magnetic resonance images Open
Purpose To demonstrate accurate MR image reconstruction from undersampled k‐space data using cross‐domain convolutional neural networks (CNNs) Methods Cross‐domain CNNs consist of 3 components: (1) a deep CNN operating on the k‐space (KCNN…
View article: High‐SNR multiple <i>T</i><sub>2</sub>(*)‐contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics
High‐SNR multiple <i>T</i><sub>2</sub>(*)‐contrast magnetic resonance imaging using a robust denoising method based on tissue characteristics Open
Purpose To develop an effective method that can suppress noise in successive multiecho T 2 (*)‐weighted magnetic resonance (MR) brain images while preventing filtering artifacts. Materials and Methods For the simulation experiments, we use…