@inproceedings {Bordeaux, France, title = {A Data-Driven Approach for Automated Integrated Circuit Segmentation of Scan Electron Microscopy Images}, year = {2022}, month = {10/2022}, address = {Bordeaux, France}, abstract = {

This paper proposes an automated data-driven integrated
circuit segmentation approach of scan electron microscopy
(SEM) images inspired by state-of-the-art CNN-based image
perception methods. Based on the requirements derived from
real industry applications, we take wire segmentation and via
detection algorithms to generate integrated circuit segmentation
maps from SEMs in our approach. On SEM images
collected in the industrial applications, our method achieves
an average of 50.71 on Electrically Significant Difference
(ESD) in the wire segmentation task and 99.05\% F1 score
in the via detection task, which achieves about 85\% and 8\%
improvements over the reference method, respectively.

}, keywords = {psclab}, author = {Zifan Yu and Bruno Machado Trindade and Michael Green and Zhikang Zhang and Pullela Sneha and Bank-Tavakoli, Erfan and Christopher Pawlowicz and Ren, Fengbo} } @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 = {

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 \< 0.05) than previously reported approaches and implementations.

}, 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 {2016, title = {A Data-Driven Compressive Sensing Framework Tailored For Energy-Efficient Wearable Sensing}, year = {2017}, month = {03/2017}, pages = {861-865}, address = {New Orleans, LA}, abstract = {

Compressive sensing (CS) is a promising technology for realizing energy-efficient wireless sensors for long-term health monitoring. However, conventional model-driven CS frameworks suffer from limited compression ratio and reconstruction quality when dealing with physiological signals due to inaccurate models and the overlook of individual variability. In this paper, we propose a data-driven CS framework that can learn signal characteristics and personalized features from any individual recording of physiologic signals to enhance CS performance with a minimized number of measurements. Such improvements are accomplished by a co-training approach that optimizes the sensing matrix and the dictionary towards improved restricted isometry property and signal sparsity, respectively. Experimental results upon ECG signals show that the proposed method, at a compression ratio of 10x, successfully reduces the isometry constant of the trained sensing matrices by 86\% against random matrices and improves the overall reconstructed signal-to-noise ratio by 15dB over conventional model-driven approaches.\ 

}, keywords = {psclab}, author = {Xu, Kai and Yixing Li and Ren, Fengbo} } @article {2016, title = {Data-Driven Sampling Matrix Boolean Optimization for Energy-Efficient Biomedical Signal Acquisition by Compressive Sensing}, journal = {IEEE Transactions on Biomedical Circuits and Systems}, volume = {11}, year = {2017}, month = {11/2016}, pages = {255-266}, abstract = {

Compressive sensing is widely used in biomedical applications, and the sampling matrix plays a critical role on both quality and power consumption of signal acquisition. It projects a high-dimensional vector of data into a low-dimensional subspace by matrix-vector multiplication. An optimal sampling matrix can ensure accurate data reconstruction and/or high compression ratio. Most existing optimization methods can only produce real-valued embedding matrices that result in large energy consumption during data acquisition. In this paper, we propose an efficient method that finds an optimal Boolean sampling matrix in order to reduce the energy consumption. Compared to random Boolean embedding, our data-driven Boolean sampling matrix can improve the image recovery quality by 9 dB. Moreover, in terms of sampling hardware complexity, it reduces the energy consumption by 4.6\× and the silicon area by 1.9\× over the data-driven real-valued embedding.

}, keywords = {psclab}, author = {Wang, Yuhao and Li, Xin and Xu, Kai and Ren, Fengbo and Yu, Hao} } @article {palangi2016distributed, title = {Distributed Compressive Sensing: A Deep Learning Approach.}, journal = {IEEE Trans. Signal Processing}, volume = {64}, number = {17}, year = {2016}, pages = {4504{\textendash}4518}, keywords = {Compressive Sensing, ref}, author = {Palangi, Hamid and Ward, Rabab K and Deng, Li} } @article {lecun2015deep, title = {Deep learning}, journal = {Nature}, volume = {521}, number = {7553}, year = {2015}, pages = {436{\textendash}444}, publisher = {Nature Research}, keywords = {Deep Learning, ref}, author = {LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey} } @inproceedings {mousavi2015deep, title = {A deep learning approach to structured signal recovery}, year = {2015}, pages = {1336{\textendash}1343}, publisher = {IEEE}, keywords = {Compressive Sensing, ref}, author = {Mousavi, Ali and Patel, Ankit B and Baraniuk, Richard G} } @article {schmidhuber2015deep, title = {Deep learning in neural networks: An overview}, journal = {Neural networks}, volume = {61}, year = {2015}, pages = {85{\textendash}117}, publisher = {Elsevier}, keywords = {Deep Learning, ref}, author = {Schmidhuber, J{\"u}rgen} } @booklet {lisaLab2015, title = {Deep Learning Tutorial.}, year = {2015}, publisher = {University of Montreal,}, url = {http://deeplearning.net/tutorial/deeplearning.pdf}, author = {LISA lab} } @inproceedings {gupta2015deep, title = {Deep learning with limited numerical precision}, year = {2015}, pages = {1737{\textendash}1746}, keywords = {Deep Learning, ref}, author = {Gupta, Suyog and Agrawal, Ankur and Gopalakrishnan, Kailash and Narayanan, Pritish} } @inproceedings {chen2014dadiannao, title = {Dadiannao: A machine-learning supercomputer}, year = {2014}, pages = {609{\textendash}622}, publisher = {IEEE Computer Society}, keywords = {Deep Learning, ref}, author = {Chen, Yunji and Luo, Tao and Liu, Shaoli and Zhang, Shijin and He, Liqiang and Wang, Jia and Li, Ling and Chen, Tianshi and Xu, Zhiwei and Sun, Ninghui and others} } @inproceedings {chen2014diannao, title = {Diannao: A small-footprint high-throughput accelerator for ubiquitous machine-learning}, volume = {49}, number = {4}, year = {2014}, pages = {269{\textendash}284}, publisher = {ACM}, keywords = {Deep Learning, ref}, author = {Chen, Tianshi and Du, Zidong and Sun, Ninghui and Wang, Jia and Wu, Chengyong and Chen, Yunji and Temam, Olivier} }