Review of medical data analysis based on spiking neural networks

Medical data mainly includes various types of biomedical signals and medical images, which can be used by professional doctors to make judgments on patients' health conditions. However, the interpretation of medical data requires a lot of human cost and there may be misjudgments, so many scholars use neural networks and deep learning to classify and study medical data, which can improve the efficiency and accuracy of doctors and detect diseases early for early diagnosis, etc. Therefore, it has a wide range of application prospects. However, traditional neural networks have disadvantages such as high energy consumption and high latency (slow computation speed). This paper presents recent research on signal classification and disease diagnosis based on a third-generation neural network, the spiking neuron network, using medical data including EEG signals, ECG signals, EMG signals and MRI images. The advantages and disadvantages of pulsed neural networks compared with traditional networks are summarized and its development orientation in the future is prospected

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

International Brain Tumor Segmentation (BraTS) challengeGliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.This work was supported in part by the 1) National Institute of Neurological Disorders and Stroke (NINDS) of the NIH R01 grant with award number R01-NS042645, 2) Informatics Technology for Cancer Research (ITCR) program of the NCI/NIH U24 grant with award number U24-CA189523, 3) Swiss Cancer League, under award number KFS-3979-08-2016, 4) Swiss National Science Foundation, under award number 169607.Article signat per 427 autors/es: Spyridon Bakas1,2,3,†,‡,∗ , Mauricio Reyes4,† , Andras Jakab5,†,‡ , Stefan Bauer4,6,169,† , Markus Rempfler9,65,127,† , Alessandro Crimi7,† , Russell Takeshi Shinohara1,8,† , Christoph Berger9,† , Sung Min Ha1,2,† , Martin Rozycki1,2,† , Marcel Prastawa10,† , Esther Alberts9,65,127,† , Jana Lipkova9,65,127,† , John Freymann11,12,‡ , Justin Kirby11,12,‡ , Michel Bilello1,2,‡ , Hassan M. Fathallah-Shaykh13,‡ , Roland Wiest4,6,‡ , Jan Kirschke126,‡ , Benedikt Wiestler126,‡ , Rivka Colen14,‡ , Aikaterini Kotrotsou14,‡ , Pamela Lamontagne15,‡ , Daniel Marcus16,17,‡ , Mikhail Milchenko16,17,‡ , Arash Nazeri17,‡ , Marc-Andr Weber18,‡ , Abhishek Mahajan19,‡ , Ujjwal Baid20,‡ , Elizabeth Gerstner123,124,‡ , Dongjin Kwon1,2,† , Gagan Acharya107, Manu Agarwal109, Mahbubul Alam33 , Alberto Albiol34, Antonio Albiol34, Francisco J. Albiol35, Varghese Alex107, Nigel Allinson143, Pedro H. A. Amorim159, Abhijit Amrutkar107, Ganesh Anand107, Simon Andermatt152, Tal Arbel92, Pablo Arbelaez134, Aaron Avery60, Muneeza Azmat62, Pranjal B.107, Wenjia Bai128, Subhashis Banerjee36,37, Bill Barth2 , Thomas Batchelder33, Kayhan Batmanghelich88, Enzo Battistella42,43 , Andrew Beers123,124, Mikhail Belyaev137, Martin Bendszus23, Eze Benson38, Jose Bernal40 , Halandur Nagaraja Bharath141, George Biros62, Sotirios Bisdas76, James Brown123,124, Mariano Cabezas40, Shilei Cao67, Jorge M. Cardoso76, Eric N Carver41, Adri Casamitjana138, Laura Silvana Castillo134, Marcel Cat138, Philippe Cattin152, Albert Cerigues ´ 40, Vinicius S. Chagas159 , Siddhartha Chandra42, Yi-Ju Chang45, Shiyu Chang156, Ken Chang123,124, Joseph Chazalon29 , Shengcong Chen25, Wei Chen46, Jefferson W Chen80, Zhaolin Chen130, Kun Cheng120, Ahana Roy Choudhury47, Roger Chylla60, Albert Clrigues40, Steven Colleman141, Ramiro German Rodriguez Colmeiro149,150,151, Marc Combalia138, Anthony Costa122, Xiaomeng Cui115, Zhenzhen Dai41, Lutao Dai50, Laura Alexandra Daza134, Eric Deutsch43, Changxing Ding25, Chao Dong65 , Shidu Dong155, Wojciech Dudzik71,72, Zach Eaton-Rosen76, Gary Egan130, Guilherme Escudero159, Tho Estienne42,43, Richard Everson87, Jonathan Fabrizio29, Yong Fan1,2 , Longwei Fang54,55, Xue Feng27, Enzo Ferrante128, Lucas Fidon42, Martin Fischer95, Andrew P. French38,39 , Naomi Fridman57, Huan Fu90, David Fuentes58, Yaozong Gao68, Evan Gates58, David Gering60 , Amir Gholami61, Willi Gierke95, Ben Glocker128, Mingming Gong88,89, Sandra Gonzlez-Vill40, T. Grosges151, Yuanfang Guan108, Sheng Guo64, Sudeep Gupta19, Woo-Sup Han63, Il Song Han63 , Konstantin Harmuth95, Huiguang He54,55,56, Aura Hernndez-Sabat100, Evelyn Herrmann102 , Naveen Himthani62, Winston Hsu111, Cheyu Hsu111, Xiaojun Hu64, Xiaobin Hu65, Yan Hu66, Yifan Hu117, Rui Hua68,69, Teng-Yi Huang45, Weilin Huang64, Sabine Van Huffel141, Quan Huo68, Vivek HV70, Khan M. Iftekharuddin33, Fabian Isensee22, Mobarakol Islam81,82, Aaron S. Jackson38 , Sachin R. Jambawalikar48, Andrew Jesson92, Weijian Jian119, Peter Jin61, V Jeya Maria Jose82,83 , Alain Jungo4 , Bernhard Kainz128, Konstantinos Kamnitsas128, Po-Yu Kao79, Ayush Karnawat129 , Thomas Kellermeier95, Adel Kermi74, Kurt Keutzer61, Mohamed Tarek Khadir75, Mahendra Khened107, Philipp Kickingereder23, Geena Kim135, Nik King60, Haley Knapp60, Urspeter Knecht4 , Lisa Kohli60, Deren Kong64, Xiangmao Kong115, Simon Koppers32, Avinash Kori107, Ganapathy Krishnamurthi107, Egor Krivov137, Piyush Kumar47, Kaisar Kushibar40, Dmitrii Lachinov84,85 , Tryphon Lambrou143, Joon Lee41, Chengen Lee111, Yuehchou Lee111, Matthew Chung Hai Lee128 , Szidonia Lefkovits96, Laszlo Lefkovits97, James Levitt62, Tengfei Li51, Hongwei Li65, Wenqi Li76,77 , Hongyang Li108, Xiaochuan Li110, Yuexiang Li133, Heng Li51, Zhenye Li146, Xiaoyu Li67, Zeju Li158 , XiaoGang Li162, Wenqi Li76,77, Zheng-Shen Lin45, Fengming Lin115, Pietro Lio153, Chang Liu41 , Boqiang Liu46, Xiang Liu67, Mingyuan Liu114, Ju Liu115,116, Luyan Liu112, Xavier Llado´ 40, Marc Moreno Lopez132, Pablo Ribalta Lorenzo72, Zhentai Lu53, Lin Luo31, Zhigang Luo162, Jun Ma73 , Kai Ma117, Thomas Mackie60, Anant Madabhushi129, Issam Mahmoudi74, Klaus H. Maier-Hein22 , Pradipta Maji36, CP Mammen161, Andreas Mang165, B. S. Manjunath79, Michal Marcinkiewicz71 , Steven McDonagh128, Stephen McKenna157, Richard McKinley6 , Miriam Mehl166, Sachin Mehta91 , Raghav Mehta92, Raphael Meier4,6 , Christoph Meinel95, Dorit Merhof32, Craig Meyer27,28, Robert Miller131, Sushmita Mitra36, Aliasgar Moiyadi19, David Molina-Garcia142, Miguel A.B. Monteiro105 , Grzegorz Mrukwa71,72, Andriy Myronenko21, Jakub Nalepa71,72, Thuyen Ngo79, Dong Nie113, Holly Ning131, Chen Niu67, Nicholas K Nuechterlein91, Eric Oermann122, Arlindo Oliveira105,106, Diego D. C. Oliveira159, Arnau Oliver40, Alexander F. I. Osman140, Yu-Nian Ou45, Sebastien Ourselin76 , Nikos Paragios42,44, Moo Sung Park121, Brad Paschke60, J. Gregory Pauloski58, Kamlesh Pawar130, Nick Pawlowski128, Linmin Pei33, Suting Peng46, Silvio M. Pereira159, Julian Perez-Beteta142, Victor M. Perez-Garcia142, Simon Pezold152, Bao Pham104, Ashish Phophalia136 , Gemma Piella101, G.N. Pillai109, Marie Piraud65, Maxim Pisov137, Anmol Popli109, Michael P. Pound38, Reza Pourreza131, Prateek Prasanna129, Vesna Pr?kovska99, Tony P. Pridmore38, Santi Puch99, lodie Puybareau29, Buyue Qian67, Xu Qiao46, Martin Rajchl128, Swapnil Rane19, Michael Rebsamen4 , Hongliang Ren82, Xuhua Ren112, Karthik Revanuru139, Mina Rezaei95, Oliver Rippel32, Luis Carlos Rivera134, Charlotte Robert43, Bruce Rosen123,124, Daniel Rueckert128 , Mohammed Safwan107, Mostafa Salem40, Joaquim Salvi40, Irina Sanchez138, Irina Snchez99 , Heitor M. Santos159, Emmett Sartor160, Dawid Schellingerhout59, Klaudius Scheufele166, Matthew R. Scott64, Artur A. Scussel159, Sara Sedlar139, Juan Pablo Serrano-Rubio86, N. Jon Shah130 , Nameetha Shah139, Mazhar Shaikh107, B. Uma Shankar36, Zeina Shboul33, Haipeng Shen50 , Dinggang Shen113, Linlin Shen133, Haocheng Shen157, Varun Shenoy61, Feng Shi68, Hyung Eun Shin121, Hai Shu52, Diana Sima141, Matthew Sinclair128, Orjan Smedby167, James M. Snyder41 , Mohammadreza Soltaninejad143, Guidong Song145, Mehul Soni107, Jean Stawiaski78, Shashank Subramanian62, Li Sun30, Roger Sun42,43, Jiawei Sun46, Kay Sun60, Yu Sun69, Guoxia Sun115 , Shuang Sun115, Yannick R Suter4 , Laszlo Szilagyi97, Sanjay Talbar20, Dacheng Tao26, Dacheng Tao90, Zhongzhao Teng154, Siddhesh Thakur20, Meenakshi H Thakur19, Sameer Tharakan62 , Pallavi Tiwari129, Guillaume Tochon29, Tuan Tran103, Yuhsiang M. Tsai111, Kuan-Lun Tseng111 , Tran Anh Tuan103, Vadim Turlapov85, Nicholas Tustison28, Maria Vakalopoulou42,43, Sergi Valverde40, Rami Vanguri48,49, Evgeny Vasiliev85, Jonathan Ventura132, Luis Vera142, Tom Vercauteren76,77, C. A. Verrastro149,150, Lasitha Vidyaratne33, Veronica Vilaplana138, Ajeet Vivekanandan60, Guotai Wang76,77, Qian Wang112, Chiatse J. Wang111, Weichung Wang111, Duo Wang153, Ruixuan Wang157, Yuanyuan Wang158, Chunliang Wang167, Guotai Wang76,77, Ning Wen41, Xin Wen67, Leon Weninger32, Wolfgang Wick24, Shaocheng Wu108, Qiang Wu115,116 , Yihong Wu144, Yong Xia66, Yanwu Xu88, Xiaowen Xu115, Peiyuan Xu117, Tsai-Ling Yang45 , Xiaoping Yang73, Hao-Yu Yang93,94, Junlin Yang93, Haojin Yang95, Guang Yang170, Hongdou Yao98, Xujiong Ye143, Changchang Yin67, Brett Young-Moxon60, Jinhua Yu158, Xiangyu Yue61 , Songtao Zhang30, Angela Zhang79, Kun Zhang89, Xuejie Zhang98, Lichi Zhang112, Xiaoyue Zhang118, Yazhuo Zhang145,146,147, Lei Zhang143, Jianguo Zhang157, Xiang Zhang162, Tianhao Zhang168, Sicheng Zhao61, Yu Zhao65, Xiaomei Zhao144,55, Liang Zhao163,164, Yefeng Zheng117 , Liming Zhong53, Chenhong Zhou25, Xiaobing Zhou98, Fan Zhou51, Hongtu Zhu51, Jin Zhu153, Ying Zhuge131, Weiwei Zong41, Jayashree Kalpathy-Cramer123,124,† , Keyvan Farahani12,†,‡ , Christos Davatzikos1,2,†,‡ , Koen van Leemput123,124,125,† , and Bjoern Menze9,65,127,†,∗Preprin

Multi-scale Evolutionary Neural Architecture Search for Deep Spiking Neural Networks

Spiking Neural Networks (SNNs) have received considerable attention not only for their superiority in energy efficient with discrete signal processing, but also for their natural suitability to integrate multi-scale biological plasticity. However, most SNNs directly adopt the structure of the well-established DNN, rarely automatically design Neural Architecture Search (NAS) for SNNs. The neural motifs topology, modular regional structure and global cross-brain region connection of the human brain are the product of natural evolution and can serve as a perfect reference for designing brain-inspired SNN architecture. In this paper, we propose a Multi-Scale Evolutionary Neural Architecture Search (MSE-NAS) for SNN, simultaneously considering micro-, meso- and macro-scale brain topologies as the evolutionary search space. MSE-NAS evolves individual neuron operation, self-organized integration of multiple circuit motifs, and global connectivity across motifs through a brain-inspired indirect evaluation function, Representational Dissimilarity Matrices (RDMs). This training-free fitness function could greatly reduce computational consumption and NAS's time, and its task-independent property enables the searched SNNs to exhibit excellent transferbility and scalability. Extensive experiments demonstrate that the proposed algorithm achieves state-of-the-art (SOTA) performance with shorter simulation steps on static datasets (CIFAR10, CIFAR100) and neuromorphic datasets (CIFAR10-DVS and DVS128-Gesture). The thorough analysis also illustrates the significant performance improvement and consistent bio-interpretability deriving from the topological evolution at different scales and the RDMs fitness function

Intelligent Computing: The Latest Advances, Challenges and Future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human-computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing. Intelligent computing is still in its infancy and an abundance of innovations in the theories, systems, and applications of intelligent computing are expected to occur soon. We present the first comprehensive survey of literature on intelligent computing, covering its theory fundamentals, the technological fusion of intelligence and computing, important applications, challenges, and future perspectives. We believe that this survey is highly timely and will provide a comprehensive reference and cast valuable insights into intelligent computing for academic and industrial researchers and practitioners

Magnetic resonance image-based brain tumour segmentation methods : a systematic review

Background: Image segmentation is an essential step in the analysis and subsequent characterisation of brain tumours through magnetic resonance imaging. In the literature, segmentation methods are empowered by open-access magnetic resonance imaging datasets, such as the brain tumour segmentation dataset. Moreover, with the increased use of artificial intelligence methods in medical imaging, access to larger data repositories has become vital in method development. Purpose: To determine what automated brain tumour segmentation techniques can medical imaging specialists and clinicians use to identify tumour components, compared to manual segmentation. Methods: We conducted a systematic review of 572 brain tumour segmentation studies during 2015–2020. We reviewed segmentation techniques using T1-weighted, T2-weighted, gadolinium-enhanced T1-weighted, fluid-attenuated inversion recovery, diffusion-weighted and perfusion-weighted magnetic resonance imaging sequences. Moreover, we assessed physics or mathematics-based methods, deep learning methods, and software-based or semi-automatic methods, as applied to magnetic resonance imaging techniques. Particularly, we synthesised each method as per the utilised magnetic resonance imaging sequences, study population, technical approach (such as deep learning) and performance score measures (such as Dice score). Statistical tests: We compared median Dice score in segmenting the whole tumour, tumour core and enhanced tumour. Results: We found that T1-weighted, gadolinium-enhanced T1-weighted, T2-weighted and fluid-attenuated inversion recovery magnetic resonance imaging are used the most in various segmentation algorithms. However, there is limited use of perfusion-weighted and diffusion-weighted magnetic resonance imaging. Moreover, we found that the U-Net deep learning technology is cited the most, and has high accuracy (Dice score 0.9) for magnetic resonance imaging-based brain tumour segmentation. Conclusion: U-Net is a promising deep learning technology for magnetic resonance imaging-based brain tumour segmentation. The community should be encouraged to contribute open-access datasets so training, testing and validation of deep learning algorithms can be improved, particularly for diffusion- and perfusion-weighted magnetic resonance imaging, where there are limited datasets available

Intelligent computing : the latest advances, challenges and future

Computing is a critical driving force in the development of human civilization. In recent years, we have witnessed the emergence of intelligent computing, a new computing paradigm that is reshaping traditional computing and promoting digital revolution in the era of big data, artificial intelligence and internet-of-things with new computing theories, architectures, methods, systems, and applications. Intelligent computing has greatly broadened the scope of computing, extending it from traditional computing on data to increasingly diverse computing paradigms such as perceptual intelligence, cognitive intelligence, autonomous intelligence, and human computer fusion intelligence. Intelligence and computing have undergone paths of different evolution and development for a long time but have become increasingly intertwined in recent years: intelligent computing is not only intelligence-oriented but also intelligence-driven. Such cross-fertilization has prompted the emergence and rapid advancement of intelligent computing

Algorithm and Hardware Co-design for Learning On-a-chip

abstract: Machine learning technology has made a lot of incredible achievements in recent years. It has rivalled or exceeded human performance in many intellectual tasks including image recognition, face detection and the Go game. Many machine learning algorithms require huge amount of computation such as in multiplication of large matrices. As silicon technology has scaled to sub-14nm regime, simply scaling down the device cannot provide enough speed-up any more. New device technologies and system architectures are needed to improve the computing capacity. Designing specific hardware for machine learning is highly in demand. Efforts need to be made on a joint design and optimization of both hardware and algorithm. For machine learning acceleration, traditional SRAM and DRAM based system suffer from low capacity, high latency, and high standby power. Instead, emerging memories, such as Phase Change Random Access Memory (PRAM), Spin-Transfer Torque Magnetic Random Access Memory (STT-MRAM), and Resistive Random Access Memory (RRAM), are promising candidates providing low standby power, high data density, fast access and excellent scalability. This dissertation proposes a hierarchical memory modeling framework and models PRAM and STT-MRAM in four different levels of abstraction. With the proposed models, various simulations are conducted to investigate the performance, optimization, variability, reliability, and scalability. Emerging memory devices such as RRAM can work as a 2-D crosspoint array to speed up the multiplication and accumulation in machine learning algorithms. This dissertation proposes a new parallel programming scheme to achieve in-memory learning with RRAM crosspoint array. The programming circuitry is designed and simulated in TSMC 65nm technology showing 900X speedup for the dictionary learning task compared to the CPU performance. From the algorithm perspective, inspired by the high accuracy and low power of the brain, this dissertation proposes a bio-plausible feedforward inhibition spiking neural network with Spike-Rate-Dependent-Plasticity (SRDP) learning rule. It achieves more than 95% accuracy on the MNIST dataset, which is comparable to the sparse coding algorithm, but requires far fewer number of computations. The role of inhibition in this network is systematically studied and shown to improve the hardware efficiency in learning.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201