no code implementations • 4 Apr 2023 • Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, Gyeong-Moon Park, Sung-Ho Bae, Lik-Hang Lee, Pan Hui, In So Kweon, Choong Seon Hong
Overall, this work is the first to survey ChatGPT with a comprehensive review of its underlying technology, applications, and challenges.
no code implementations • 26 Mar 2023 • Soyoun Won, Sung-Ho Bae, Seong Tae Kim
Data augmentation strategies are actively used when training deep neural networks (DNNs).
1 code implementation • CVPR 2023 • Yong Hyun Ahn, Gyeong-Moon Park, Seong Tae Kim
In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data.
1 code implementation • 9 Oct 2022 • Samra Irshad, Douglas P. S. Gomes, Seong Tae Kim
To address the problem of abdominal multi-organ segmentation, we train the 3D encoder-decoder network to simultaneously segment the abdominal organs and their corresponding boundaries in CT scans via multi-task learning.
no code implementations • 25 Jul 2022 • Felix Buchert, Nassir Navab, Seong Tae Kim
By considering the consistency information with the diversity in the consistency-based embedding scheme, the proposed method could select more informative samples for labeling in the semi-supervised learning setting.
no code implementations • 4 Apr 2022 • Ashkan Khakzar, Yawei Li, Yang Zhang, Mirac Sanisoglu, Seong Tae Kim, Mina Rezaei, Bernd Bischl, Nassir Navab
One challenging property lurking in medical datasets is the imbalanced data distribution, where the frequency of the samples between the different classes is not balanced.
no code implementations • 21 Mar 2022 • Tobias Czempiel, Coco Rogers, Matthias Keicher, Magdalini Paschali, Rickmer Braren, Egon Burian, Marcus Makowski, Nassir Navab, Thomas Wendler, Seong Tae Kim
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
1 code implementation • NeurIPS 2021 • Yang Zhang, Ashkan Khakzar, Yawei Li, Azade Farshad, Seong Tae Kim, Nassir Navab
We propose a method to identify features with predictive information in the input domain.
no code implementations • 3 Oct 2021 • Michelle Xiao-Lin Foo, Seong Tae Kim, Magdalini Paschali, Leili Goli, Egon Burian, Marcus Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
1 code implementation • 4 Apr 2021 • Ashkan Khakzar, Sabrina Musatian, Jonas Buchberger, Icxel Valeriano Quiroz, Nikolaus Pinger, Soroosh Baselizadeh, Seong Tae Kim, Nassir Navab
We present our findings using publicly available chest pathologies (CheXpert, NIH ChestX-ray8) and COVID-19 datasets (BrixIA, and COVID-19 chest X-ray segmentation dataset).
1 code implementation • 1 Apr 2021 • Ashkan Khakzar, Yang Zhang, Wejdene Mansour, Yuezhi Cai, Yawei Li, Yucheng Zhang, Seong Tae Kim, Nassir Navab
Neural networks have demonstrated remarkable performance in classification and regression tasks on chest X-rays.
2 code implementations • CVPR 2021 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
Is critical input information encoded in specific sparse pathways within the neural network?
no code implementations • 19 Mar 2021 • Aadhithya Sankar, Matthias Keicher, Rami Eisawy, Abhijeet Parida, Franz Pfister, Seong Tae Kim, Nassir Navab
Disentangled representations can be useful in many downstream tasks, help to make deep learning models more interpretable, and allow for control over features of synthetically generated images that can be useful in training other models that require a large number of labelled or unlabelled data.
1 code implementation • 12 Mar 2021 • Seong Tae Kim, Leili Goli, Magdalini Paschali, Ashkan Khakzar, Matthias Keicher, Tobias Czempiel, Egon Burian, Rickmer Braren, Nassir Navab, Thomas Wendler
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 and consolidation.
no code implementations • 5 Mar 2021 • Tobias Czempiel, Magdalini Paschali, Daniel Ostler, Seong Tae Kim, Benjamin Busam, Nassir Navab
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
no code implementations • 1 Jan 2021 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
Is critical input information encoded in specific sparse paths within the network?
no code implementations • 10 Nov 2020 • Abinav Ravi Venkatakrishnan, Seong Tae Kim, Rami Eisawy, Franz Pfister, Nassir Navab
To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported.
no code implementations • 21 May 2020 • Hong Joo Lee, Seong Tae Kim, Hakmin Lee, Nassir Navab, Yong Man Ro
Experimental results show that the proposed method could provide useful uncertainty information by Bayesian approximation with the efficient ensemble model generation and improve the predictive performance.
no code implementations • 21 May 2020 • Hakmin Lee, Hong Joo Lee, Seong Tae Kim, Yong Man Ro
After the ensemble models are trained, it can hide the gradient efficiently and avoid the gradient-based attack by the random layer sampling method.
1 code implementation • 7 Apr 2020 • Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab
Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation.
no code implementations • 5 Apr 2020 • Seong Tae Kim, Farrukh Mushtaq, Nassir Navab
Active learning is one of the solutions to this problem where an active learner is designed to indicate which samples need to be annotated to effectively train a target model.
2 code implementations • 24 Mar 2020 • Tobias Czempiel, Magdalini Paschali, Matthias Keicher, Walter Simson, Hubertus Feussner, Seong Tae Kim, Nassir Navab
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 systems.
Ranked #3 on
Surgical phase recognition
on Cholec80
1 code implementation • 26 Feb 2020 • Maria Tirindelli, Maria Victorova, Javier Esteban, Seong Tae Kim, David Navarro-Alarcon, Yong Ping Zheng, Nassir Navab
Processed force and ultrasound data are fused using a 1D Convolutional Network to compute the location of the vertebral levels.
no code implementations • 25 Nov 2019 • Ashkan Khakzar, Soroosh Baselizadeh, Saurabh Khanduja, Christian Rupprecht, Seong Tae Kim, Nassir Navab
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.
no code implementations • 10 Jun 2019 • 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 performance.
no code implementations • 17 Sep 2018 • Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, Yong Man Ro
In order to learn deep network model to be well-behaved in bio-image computing fields, a lot of labeled data is required.
no code implementations • 23 May 2018 • Seong Tae Kim, Hakmin Lee, Hak Gu Kim, Yong Man Ro
In this paper, we investigate interpretability in CADx with the proposed interpretable CADx (ICADx) framework.
no code implementations • ECCV 2018 • Seong Tae Kim, Yong Man Ro
In this paper, a novel deep learning approach, named facial dynamics interpreter network, has been proposed to interpret the important relations between local dynamics for estimating facial traits from expression sequence.
no code implementations • 28 Nov 2017 • Geonmo Gu, Seong Tae Kim, Kihyun Kim, Wissam J. Baddar, Yong Man Ro
through a generative model is helpful in addressing the lack of training data.
no code implementations • 31 Jul 2017 • Tae Kwan Lee, Wissam J. Baddar, Seong Tae Kim, Yong Man Ro
Our classification results on Multi-PIE dataset for facial expression recognition and CIFAR-10 dataset for object classification reveal that the compact CNN with the proposed logarithmic filter grouping scheme outperforms the same network with the uniform filter grouping in terms of accuracy and parameter efficiency.
no code implementations • 31 May 2017 • Seong Tae Kim, Yong Man Ro
In order to improve the effectiveness of the learning with instructional video, observation and evaluation of the activity are required.