@inproceedings {London, UK,
	title = {STPLS3D: A Large-Scale Synthetic and Real Aerial Photogrammetry 3D Point Cloud Dataset},
	year = {2022},
	month = {11/2022},
	address = {London, UK},
	abstract = {<p>Although various 3D datasets with different functions and scales have been proposed recently, it remains challenging for individuals to complete the whole pipeline of large- scale data collection, sanitization, and annotation. Moreover, the created datasets usually face the challenge of extremely imbalanced class distribution or partial low-quality data samples. Motivated by this, we explore the procedurally synthetic 3D data generation paradigm to equip individuals with the full capability of creating large-scale annotated photogrammetry point clouds. Specifically, we introduce a synthetic aerial photogrammetry point clouds generation pipeline that takes full advantage of open geospatial data sources and off-the-shelf commercial packages. Unlike generating synthetic data in virtual games, where the simulated data usually have limited gaming environments created by artists, the proposed pipeline simulates the reconstruction process of the real environment by following the same UAV flight pattern on different synthetic terrain shapes and building densities, which ensure similar quality, noise pattern, and diversity with real data. In addition, the precise semantic and instance annotations can be generated fully automatically, avoiding the expensive and time-consuming manual annotation. Based on\&nbsp;the proposed pipeline, we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16 km2 of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide datasets collected from four areas in the real environment. Extensive experiments conducted on our datasets demonstrate the effectiveness and quality of the proposed synthetic dataset.</p>
},
	keywords = {psclab},
	url = {https://www.stpls3d.com/},
	author = {Meida Chen and Qingyong Hu and Zifan Yu and Hugues Thomas and Andrew Feng and Yu Hou and Kyle McCullough and Fengbo Ren and Lucio Soibelman}
}
@article {366,
	title = {A Deep Learning Approach for Targeted Contrast-Enhanced Ultrasound Based Prostate Cancer Detection},
	journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
	volume = {16},
	year = {2019},
	month = {05/2018},
	pages = {1794-1801},
	abstract = {<p>The important role of angiogenesis in cancer development has driven many researchers to investigate the prospects of noninvasive cancer diagnosis based on the technology of contrast-enhanced ultrasound (CEUS) imaging. This paper presents a deep learning framework to detect prostate cancer in the sequential CEUS images. The proposed method uniformly extracts features from both the spatial and the temporal dimensions by performing three-dimensional convolution operations, which captures the dynamic information of the perfusion process encoded in multiple adjacent frames for prostate cancer detection. The deep learning models were trained and validated against expert delineations over the CEUS images recorded using two types of contrast agents, i.e. the anti-PSMA based agent targeted to prostate cancer cells and the non-targeted blank agent. Experiments showed that the deep learning method achieved over 91\% specificity and 90\% average accuracy over the targeted CEUS images for prostate cancer detection, which was superior (p \&lt; 0.05) than previously reported approaches and implementations.</p>
},
	keywords = {psclab},
	doi = {10.1109/TCBB.2018.2835444},
	url = {https://ieeexplore.ieee.org/document/8357938},
	author = {Yujie Feng and Fan Yang and Xichuan Zhou and Yanli Guo and Fang Tang and Fengbo Ren and Jishun Guo and Shuiwang Ji}
}
@inproceedings {rastegari2016xnor,
	title = {Xnor-net: Imagenet classification using binary convolutional neural networks},
	year = {2016},
	pages = {525{\textendash}542},
	publisher = {Springer},
	keywords = {Deep Learning, ref},
	author = {Rastegari, Mohammad and Ordonez, Vicente and Redmon, Joseph and Farhadi, Ali}
}
@article {putnam2015reconfigurable,
	title = {A reconfigurable fabric for accelerating large-scale datacenter services},
	journal = {IEEE Micro},
	volume = {35},
	number = {3},
	year = {2015},
	pages = {10{\textendash}22},
	publisher = {IEEE},
	keywords = {Deep Learning, ref},
	author = {Putnam, Andrew and Caulfield, Adrian M and Chung, Eric S and Chiou, Derek and Constantinides, Kypros and Demme, John and Esmaeilzadeh, Hadi and Fowers, Jeremy and Gopal, Gopi Prashanth and Gray, Jan and others}
}
@inproceedings {157,
	title = {From OpenCL to high-performance hardware on FPGAs},
	year = {2012},
	pages = {531{\textendash}534},
	publisher = {IEEE},
	keywords = {Parallel and Reconfigurable Computing, ref},
	author = {Czajkowski, Tomasz S and Aydonat, Utku and Denisenko, Dmitry and Freeman, John and Kinsner, Michael and Neto, David and Wong, Jason and Yiannacouras, Peter and Singh, Deshanand P}
}
@inproceedings {perez2009supervised,
	title = {A supervised learning method for neural networks based on sensitivity analysis with automatic regularization},
	year = {2009},
	pages = {157{\textendash}164},
	publisher = {Springer},
	keywords = {Deep Learning, ref},
	author = {P{\'e}rez-S{\'a}nchez, Beatriz and Fontenla-Romero, Oscar and Guijarro-Berdi{\~n}as, Bertha}
}
@article {figueiredo2007gradient,
	title = {Gradient projection for sparse reconstruction: Application to compressed sensing and other inverse problems},
	journal = {IEEE Journal of selected topics in signal processing},
	volume = {1},
	number = {4},
	year = {2007},
	pages = {586{\textendash}597},
	publisher = {IEEE},
	keywords = {Compressive Sensing, ref},
	author = {Figueiredo, M{\'a}rio AT and Nowak, Robert D and Wright, Stephen J}
}
@book {friedman2001elements,
	title = {The elements of statistical learning},
	volume = {1},
	year = {2001},
	publisher = {Springer series in statistics New York},
	organization = {Springer series in statistics New York},
	keywords = {Deep Learning, ref},
	url = {https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about},
	author = {Friedman, Jerome and Hastie, Trevor and Tibshirani, Robert}
}
