232 citations · 278 across the 15 of their papers we have counts for
17 papers
Improved Abdominal Multi-Organ Segmentation via 3D Boundary-Constrained Deep Neural Networks
Samra Irshad, Douglas P. S. Gomes, Seong Tae Kim
Quantitative assessment of the abdominal region from clinically acquired CT scans requires the simultaneous segmentation of abdominal organs. Thanks to the availability of high-per…
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Seong Tae Kim, Leili Goli, Magdalini Paschali +7
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity a…
Self-Supervised Out-of-Distribution Detection in Brain CT Scans
Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy +2
Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation…
Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
Hong Joo Lee, Seong Tae Kim, Hakmin Lee +2
Recent studies have shown that ensemble approaches could not only improve accuracy and but also estimate model uncertainty in deep learning. However, it requires a large number of…
Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
Hakmin Lee, Hong Joo Lee, Seong Tae Kim +1
Deep neural networks have achieved substantial achievements in several computer vision areas, but have vulnerabilities that are often fooled by adversarial examples that are not re…
Confident Coreset for Active Learning in Medical Image Analysis
Seong Tae Kim, Farrukh Mushtaq, Nassir Navab
Recent advances in deep learning have resulted in great successes in various applications. Although semi-supervised or unsupervised learning methods have been widely investigated,…
Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
Stefan Denner, Ashkan Khakzar, Moiz Sajid +4
Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cu…
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Tobias Czempiel, Magdalini Paschali, Matthias Keicher +4
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support…
Force-Ultrasound Fusion: Bringing Spine Robotic-US to the Next "Level"
Maria Tirindelli, Maria Victorova, Javier Esteban +4
Spine injections are commonly performed in several clinical procedures. The localization of the target vertebral level (i.e. the position of a vertebra in a spine) is typically don…
Improving Feature Attribution through Input-specific Network Pruning
Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja +3
Attributing the output of a neural network to the contribution of given input elements is a way of shedding light on the black-box nature of neural networks. Due to the complexity…
Generation of Multimodal Justification Using Visual Word Constraint Model for Explainable Computer-Aided Diagnosis
Hyebin Lee, Seong Tae Kim, Yong Man Ro
The ambiguity of the decision-making process has been pointed out as the main obstacle to applying the deep learning-based method in a practical way in spite of its outstanding per…
Feature2Mass: Visual Feature Processing in Latent Space for Realistic Labeled Mass Generation
Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee +1
This paper deals with a method for generating realistic labeled masses. Recently, there have been many attempts to apply deep learning to various bio-image computing fields includi…
ICADx: Interpretable computer aided diagnosis of breast masses
Seong Tae Kim, Hakmin Lee, Hak Gu Kim +1
In this study, a novel computer aided diagnosis (CADx) framework is devised to investigate interpretability for classifying breast masses. Recently, a deep learning technology has…
Differential Generative Adversarial Networks: Synthesizing Non-linear Facial Variations with Limited Number of Training Data
Geonmo Gu, Seong Tae Kim, Kihyun Kim +2
In face-related applications with a public available dataset, synthesizing non-linear facial variations (e.g., facial expression, head-pose, illumination, etc.) through a generativ…
Facial Dynamics Interpreter Network: What are the Important Relations between Local Dynamics for Facial Trait Estimation?
Seong Tae Kim, Yong Man Ro
Human face analysis is an important task in computer vision. According to cognitive-psychological studies, facial dynamics could provide crucial cues for face analysis. The motion…
Convolution with Logarithmic Filter Groups for Efficient Shallow CNN
Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim +1
In convolutional neural networks (CNNs), the filter grouping in convolution layers is known to be useful to reduce the network parameter size. In this paper, we propose a new logar…
EvaluationNet: Can Human Skill be Evaluated by Deep Networks?
Seong Tae Kim, Yong Man Ro
With the recent substantial growth of media such as YouTube, a considerable number of instructional videos covering a wide variety of tasks are available online. Therefore, online…