Publications

2019

[C22] Building a breast-sentence dataset: Its usefulness for computer-aided diagnosis [accepted]

    • Hyebin Lee, Seong Tae Kim, and Yong Man Ro
    • ICCV Workshop on Visual Recognition for Medical Images, South Korea

[C21] Realistic breast mass generation through BIRADS category [accepted]

    • Hakmin Lee, Seong Tae Kim, Jae-Hyeok Lee, and Yong Man Ro
    • International Conference on Medical Image Computing and Computer-Assisted Intervention, China

[C20] Generation of multimodal generation using visual word constraint model for explainable computer-aided diagnosis

    • Hyebin Lee, Seong Tae Kim, and Yong Man Ro
    • MICCAI Workshop on Interpretability of Machine Intelligence in Medical Image Computing, China [Demo]

[C19] Probenet: Probing deep networks

    • Jae-Hyeok Lee, Seong Tae Kim, and Yong Man Ro
    • IEEE International Conference on Image Processing, Taiwan

[J8] Attended relation feature representation of facial dynamics for facial authentication

    • Seong Tae Kim and Yong Man Ro
    • IEEE Transactions on Information Forensics and Security (IF: 6.211)

[J7] Lightweight and effective facial landmark detection using adversarial learning with face geometric map generative network

    • Hong Joo Lee, Seong Tae Kim, Hakmin Lee, and Yong Man Ro
    • IEEE Transactions on Circuit Systems for Video Technology (IF: 4.046)

[J6] Implementation of multimodal biometric recognition via multi-feature deep learning networks and feature fusion

    • Leslie Ching Ow Tiong, Seong Tae Kim, and Yong Man Ro
    • Multimedia Tools and Applications (IF: 2.101)

[C18] Visual evidence for interpreting diagnostic decision of deep neural network in computer-aided diagnosis

    • Seong Tae Kim, Jae-Hyeok Lee, and Yong Man Ro
    • SPIE Medical Imaging, USA

2018

[C17] Facial dynamics interpreter network: What are the important relations between local dynamics for facial trait estimation?

    • Seong Tae Kim and Yong Man Ro
    • European Conference on Computer Vision Workshop (ECCV), Germany [poster][Demo]

[J5] Visually interpretable deep network for diagnosis of breast masses on mammograms

    • Seong Tae Kim, Jae-Hyeok Lee, Hakmin Lee, and Yong Man Ro
    • Physics in Medicine and Biology (IF: 3.030)

[C16] Feature2Mass: Visual feature processing in latent space for realistic labeled mass generation

    • Jae-Hyeok Lee, Seong Tae Kim, Hakmin Lee, and Yong Man Ro
    • European Conference on Computer Vision Workshop (ECCVW), Germany [poster]

[C15] ICADx: Interpretable computer aided diagnosis of breast masses

    • Seong Tae Kim, Hakmin Lee, Hak Gu Kim, and Yong Man Ro
    • SPIE Medical Imaging, USA (Robert F. Wagner All Conference Best Student Paper Final Lists Award, Oral presentation)

[C14] Convolution with logarithmic filter groups for efficient shallow CNN

    • Tae Kwan Lee, Wissam J Baddar, Seong Tae Kim, Yong Man Ro
    • International Conference on Multimedia Modeling, Thailand (Oral presentation)

[C13] Teacher and student joint learning for compact facial landmark detection network

    • Hongju Lee, Wissam Baddar, Hak Gu Kim, Seong Tae Kim, and Yong Man Ro
    • International Conference on Multimedia Modeling, Thailand (Oral presentation)

2017

[J4] Latent feature representation with depth directional long-term recurrent learning for breast masses in digital breast tomosynthesis

    • Dae Hoe Kim, Seong Tae Kim, Jung Min Chang, and Yong Man Ro
    • Physics in Medicine and Biology (IF: 3.030)

[T2] Differential generative adversarial networks: Synthesizing non-linear facial variations with limited number of training data

    • Geonmo Gu, Seong Tae Kim, Kihyun Kim, Wissam J Baddar, Yong Man Ro
    • Technical Report [arXiv:1711.10267]

[C12] Multi-scale facial scanning via spatial LSTM for latent facial feature representation

    • Seong Tae Kim, Yeoreum Choi, and Yong Man Ro
    • International Conference of the Biometrics Special Interest Group , Germany

[C11] Adaptive attention fusion network for visual question answering

    • Geonmo Gu, Seong Tae Kim, and Yong Man Ro
    • IEEE International Conference on Multimedia and Expo (ICME), Hong Kong [Demo]

[K8] Multimodal face biometrics by using convolutional neural networks

    • Leslie Ching Ow Tiong, Seong Tae Kim, and Yong Man Ro
    • Journal of Korea Multimedia Society

[T1] EvaluationNet: Can human skill be evaluated by deep networks?

2016

[C10] Facial dynamic modelling using long short-term memory network: Analysis and application to face authentication

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • IEEE International Conference on Biometrics: Thoery, Applications, and Systems (BTAS), USA [poster] [Demo]

[C9] Spatio-temporal representation for face authentication by using multi-task learning with human attributes

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • IEEE International Conference on Image Processing (ICIP), USA [poster]

[C8] A deep facial landmarks detection with facial contour and facial components constraint

    • Wissam Baddar, Jisoo Son, Dae Hoe Kim, Seong Tae Kim, Yong Man Ro
    • IEEE International Conference on Image Processing (ICIP), USA [poster]

[K7] Object tracking using siamese CNN structure considering object movement

    • Jung-Uk Kim, Hyung-Il Kim, Seong Tae Kim, Yong Man Ro
    • Proceeding of Korea Multimedia Society, Korea

[K6] 3D-DCNN based spatio-temporal latent feature analysis of facial motion

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • Proceeding of Korea Multimedia Society, Korea

[K5] Deep learning feature representation by emphasizing expression change for subtle facial expression recognition

    • Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
    • Proceeding of Korea Multimedia Society, Korea (Best Paper Award)

[C7] Latent feature representation with 3-D Multi-view convolutional neural network for bilateral analysis in digital breast tomosynthesis

    • Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
    • IEEE International Conference on Acoustics, speech and signal processing (ICASSP), China

2015

[J3] Detection of masses in digital breast tomosynthesis using complementary information of simulated projection

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • Medical Physics (IF: 3.177)

[J2] Improving mass detection using combined feature representations from projection views and reconstructed volume of DBT and boosting based classification with feature selection

    • Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
    • Physics in Medicine and Biology (IF: 3.030) [Demo]

[C6] Region matching based on local structure information in ipsilateral digital breast tomosynthesis views

    • Seong Tae Kim, Dae Hoe Kim, Dong Jin Ji, and Yong Man Ro
    • IEEE International Conference on Image Processing (ICIP), Canada [poster]

[C5] Feature extraction from bilateral dissimilarity in digital breast tomosynthesis reconstructed volume

    • Dae Hoe Kim, Seong Tae Kim, Wissam J Baddar, and Yong Man Ro
    • IEEE International Conference on Image Processing (ICIP), Canada (Selected as Top 10% paper) [poster]

[C4] Combination of conspicuity improved synthetic mammograms and digital breast tomosynthesis: A promising approach for mass detection

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • SPIE Medical Imaging, USA (Oral presentation)

[C3] Feature extraction from inter-view similarity of DBT projection views

    • Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
    • SPIE Medical Imaging, USA (Best poster award) [poster]

~2014

[J1] Breast mass detection using slice conspicuity in 3D reconstructed digital breast volumes

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • Physics in Medicine and Biology (IF: 3.030), 2014

[C2] Generation of conspicuity-improved synthetic image from digital breast tomosynthesis

    • Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • International Conference on Digital Signal Processing (DSP), Hong Kong, 2014 (Oral presentation)

[K4] Conspicuity based central slice estimation in 3D DBT reconstructed volume

    • Dae Hoe Kim, Seong Tae Kim, and Yong Man Ro
    • Proceeding of Korea Society of Medical & Biological Engineering, Korea, 2014

[C1] Mass detection based on pooled mass probability map of 3D reconstructed slices in digital breast tomosynthesis

    • Seong Tae Kim, Dae Hoe Kim, Eun Suk Cha, and Yong Man Ro
    • IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), Spain, 2014 (Oral presentation)

[K3] Improvement of sparse representation based classifier using fisher discrimination dictionary learning for malignant mass detection

    • Seong Tae Kim, Seung Hyun Lee, Hyun-seok Min, and Yong Man Ro
    • Journal of Korea Multimedia Society, vol. 16, pp. 558-565, 2013

[K2] Kidney segmentation using active shape model from ultrasound image

    • Eun Joon Kim, Seong Tae Kim, Dae Hoe Kim, and Yong Man Ro
    • Proceeding of Korea Multimedia Society, Korea, 2013

[K1] Malignant mass classification using fisher discrimination dictionary learning

    • Seong Tae Kim, Seung Hyun Lee, Hyun-seok Min, and Yong Man Ro
    • Proceeding of Korea Multimedia Society, Korea, 2012 (Best Paper Award)

Patent

Registered

[P6] Method and system for artificial intelligence based video surveillance using deep learning

KR 10-1995107 / Registration date: Jun. 2019

[P5] Explainable computer-aided diagnosis and the method thereof

KR 10-1938992 / Registration date: Jan. 2019

[P4] Apparatus and method for generating reprojection images for diagnostic feature extraction

US 10,092,263 / Registration date: Oct. 2018

[P3] System for instructional video learning and evaluation using deep learning

KR 10-1893290 / Registration date: Aug. 2018

[P2] Method and system for automatic biometric authentication based on facial spatio-temporal features

KR 10-180261 / Registration date: Nov. 2017

[P1] Lesion classification apparatus, and method of modifying lesion classification data

US 9,547,896 / Registration date: Jan. 2017


Applications

[P_7] Interactive computer-aided diagnosis method for lesion diagnosis and the system thereof, 2019 (KR)

[P_6] Method for interpreting visual evidence according to breast mass characteristics and the system thereof, 2019 (KR)

[P_5] Interpreting method for diagnostic decision of deep network using breast imaging-reporting and data system and the system thereof, 2018 (KR)

[P_4] Method for generating breast masses according to the lesion characteristics and the system thereof, 2018 (KR)

[P_3] Automated facial expression recognizing systems on N frames, methods, and computer-readable mediums there of, 2018 (KR)

[P_2] Analysis method of relations of face movements and the system thereof, 2018 (KR)

[P_1] Machine learning system using joint learning and the method thereof, 2018 (KR)