List of Publications
DeepECA: an end-to-end learning framework for protein contact prediction from a multiple sequence alignment
Hiroyuki Fukuda, Kentaro Tomii
BMC Bioinformatics 2020; 21(10)
A Point-Wise LiDAR and Image Multimodal Fusion Network (PMNet) for Aerial Point Cloud 3D Semantic Segmentation
Vinayaraj Poliyapram, Weimin Wang and Ryosuke Nakamura
3D semantic segmentation of point cloud aims at assigning semantic labels to each point by utilizing and respecting the 3D representation of the data. Detailed 3D semantic segmentation of urban areas can assist policymakers, insurance companies, governmental agencies for applications such as urban growth assessment, disaster management, and traffic supervision. The recent proliferation of remote sensing techniques has led to producing high resolution multimodal geospatial data. Nonetheless, currently, only limited technologies are available to fuse the multimodal dataset effectively. Therefore, this paper proposes a novel deep learning-based end-to-end Point-wise LiDAR and Image Multimodal Fusion Network (PMNet) for 3D segmentation of aerial point cloud by fusing aerial image features. PMNet respects basic characteristics of point cloud such as unordered, irregular format and permutation invariance. Notably, multi-view 3D scanned data can also be trained using PMNet since it considers aerial point cloud as a fully 3D representation. The proposed method was applied on two datasets (1) collected from the urban area of Osaka, Japan and (2) from the University of Houston campus, USA and its neighborhood. The quantitative and qualitative evaluation shows that PMNet outperforms other models which use non-fusion and multimodal fusion (observational-level fusion and feature-level fusion) strategies. In addition, the paper demonstrates the improved performance of the proposed model (PMNet) by over-sampling/augmenting the medium and minor classes in order to address the class-imbalance issues.
Crim1C140S mutant mice reveal the importance of cysteine 140 in the internal region 1 of CRIM1 for its physiological functions
Tatsuya Furuichi, Manami Tsukamoto, Masaki Saito, Yuriko Sato, Nobuyasu Oiji, Kazuhiro Yagami, Ryutaro Fukumura, Yoichi Gondo, Long Guo, Shiro Ikegawa, Yu Yamamori, Kentaro Tomii
Mammalian Genome 2019; 30(11-12); 329-338.
Lunar Calibration for ASTER VNIR and TIR with Observations of the Moon in 2003 and 2017
Toru Kouyama, Soushi Kato, Masakuni Kikuchi, Fumihiro Sakuma, Akira Miura, Tetsushi Tachikawa, Satoshi Tsuchida, Kenta Obata
The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra's launch in 1999. Since the launch, sensitivity degradations in ASTER's visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3-6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.
Salman Ahmed Shaikh, Akiyoshi Matono, Kyoung-Sook Kim:
A Distance-Window Based Real-Time Processing of Spatial Data Streams, Proc. 5th IEEE International Conference on Multimedia Big Data, BigMM 2019, Singapore, September 11-13, 2019.
Salman Ahmed Shaikh, Jun Lee, Akiyoshi Matono, Kyoung-Sook Kim:
A Robust and Scalable Pipeline for the Real-time Processing and Analysis of Massive 3D Spatial Streams, In The 21st International Conference on Information Integration and Web-based Applications & Services (iiWAS2019), December 2-4, 2019, Munich, Germany. (to appear)
Salman Ahmed Shaikh, Kousuke Nakabasami, Toshiyuki Amagasa, Hiroyuki Kitagawa:
Multidimensional Analysis of Big Data, Emerging Perspectives in Big Data Warehousing, pp. 198-224, IGI Global, 2019.
Actin Cytoskeletal Reorganization Function of JRAB/MICAL-L2 Is Fine-tuned by Intramolecular Interaction between First LIM Zinc Finger and C-terminal Coiled-coil Domains
Kazuhisa Miyake, Ayuko Sakane, Yuko Tsuchiya, Ikuko Sagawa, Yoko Tomida, Jiro Kasahara, Issei Imoto, Shio Watanabe, Daisuke Higo, Kenji Mizuguchi, Takuya Sasaki
Scientific Reports. 2019: 9: 12794.
Genome-Wide Analysis of Known and Potential Tetraspanins in Entamoeba histolytica
Kentaro Tomii, Herbert J. Santos, Tomoyoshi Nozaki
Genes. 2019; 10(11); 885.
Recurrent feedback CNN for Water Region Estimation from Multitemporal Satellite Images
Vinayaraj Poliyaprama, Nevrez Imamoglub, and Ryousuke Nakamura
Water region estimation is considered as one of the fundamental classification tasks in remote sensing. Several previous research works focused on traditional practices such as spectral analysis, and statistical approaches for water region estimation. However, producing a consistent global scale water estimation results are stillconsidered as relatively challenging task. On the other hand, in computer vision applications Convolutional Neural Network (CNN) emerged as greater tool for classification tasks. Recently, Recurrent Convolutional Neural
Network(R-CNN) proposed for improved classification results. Therefore, inspired from R-CNN, this research proposes a Recurrent feedback Encoder-Decoder without max-pooling for global scale water region estimation using temporal Landsat-8 images. The proposed R-CNN uses three Landsat-8 images which consist of current observation (t0) to predict water region and two previous observation of the same location (t-1, t -2), and these three temporal observation of the same location were employed for training with the ground truth labelled data (water/non-water) from the current observation. Proposed R-CNN model uses temporal input data and results in multi-temporal output for water region estimation. Experiments show promising results especially while using concatenated recurrent feedback features. The model significantly outperforms baseline model and UNet (without recurrent and feedback structure). Detailed comparison study on temporal Landsat-8 images that highly aected by sunglint, cloud and other atmospheric conditions shows that the proposed model has a potential to produce reliable water region estimation where UNet, baseline model R-CNN single model fail.