Deep Learning for Embedded Systems

bioniceyeargust2

This blog post has recent publications related to Deep Learning for Embedded Systems (e.g. computer systems in toys, biometrics, cars, kitchen equipment, medical equipment such as bionic eyes, etc).

Wikipedia defines Embedded systems as:

    An embedded system is a computer system with a dedicated function within a larger mechanical or electrical system, often with real-time computing constraints.[1][2] It is embedded as part of a complete device often including hardware and mechanical parts. Embedded systems control many devices in common use today.[3] Ninety-eight percent of all microprocessors are manufactured as components of embedded systems

Best regards,
Amund Tveit (WeChat ID: AmundTveit – Twitter: atveit)

Year  Title Author
2017   Six Degree-of-Freedom Localization of Endoscopic Capsule Robots using Recurrent Neural Networks embedded into a Convolutional Neural Network  M Turan, A Abdullah, R Jamiruddin, H Araujo
2017   Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices  W Meng, Z Gu, M Zhang, Z Wu
2017   14.1 A 2.9 TOPS/W deep convolutional neural network SoC in FD-SOI 28nm for intelligent embedded systems  G Desoli, N Chawla, T Boesch, S Singh, E Guidetti
2017   Characterization of Symbolic Rules Embedded in Deep DIMLP Networks: A Challenge to Transparency of Deep Learning  G Bologna, Y Hayashi
2017   Moving Object Detection in Heterogeneous Conditions in Embedded Systems  A Garbo, S Quer
2016   Re-architecting the on-chip memory sub-system of machine-learning accelerator for embedded devices  Y Wang, H Li, X Li
2016   DELAROSE: A Case Example of the Value of Embedded Course Content and Assessment in the Workplace  JSG Wells, M Bergin, C Ryan
2016   Neurosurgery Conference Experience Embedded within PCOM’s Clinical and Basic Neuroscience Curriculum: An Active Learning Model  J Okun, S Yocom, M McGuiness, M Bell, D Appelt
2016   Scene Parsing using Inference Embedded Deep Networks  S Bu, P Han, Z Liu, J Han
2016   Improving Deep Learning Accuracy with Noisy Autoencoders Embedded Perturbative Layers  L Xia, X Zhang, B Li
2016   Noise Robust Keyword Spotting Using Deep Neural Networks For Embedded Platforms  R Abdelmoula
2016   14.1 A 126.1 mW real-time natural UI/UX processor with embedded deep-learning core for low-power smart glasses  S Park, S Choi, J Lee, M Kim, J Park, HJ Yoo
2016   A wearable mobility aid for the visually impaired based on embedded 3D vision and deep learning  M Poggi, S Mattoccia
2016   Optimizing convolutional neural networks on embedded platforms with OpenCL  A Lokhmotov, G Fursin
2016   Demonstration Abstract: Accelerating Embedded Deep Learning Using DeepX  ND Lane, S Bhattacharya, P Georgiev, C Forlivesi
2016   Feedback recurrent neural network-based embedded vector and its application in topic model  L Li, S Gan, X Yin
2016   Human Pose Estimation from Depth Images via Inference Embedded Multi-task Learning  K Wang, S Zhai, H Cheng, X Liang, L Lin
2015   Memory Heat Map: Anomaly Detection in Real-Time Embedded Systems Using Memory Behavior  MK Yoon, S Mohan, J Choi, L Sha
2015   Accelerating real-time embedded scene labeling with convolutional networks  L Cavigelli, M Magno, L Benini
2015   Business meeting training on its head: inverted and embedded learning  E Van Praet
2015   CNN optimizations for embedded systems and FFT  A Vasilyev
2015   Learning Socially Embedded Visual Representation from Scratch  S Liu, P Cui, W Zhu, S Yang
2015   Inter-Tile Reuse Optimization Applied to Bandwidth Constrained Embedded Accelerators  M Peemen, B Mesman, H Corporaal
2015   Emotion recognition from embedded bodily expressions and speech during dyadic interactions  PM Müller, S Amin, P Verma, M Andriluka, A Bulling
2015   Incremental extreme learning machine based on deep feature embedded  J Zhang, S Ding, N Zhang, Z Shi
2015   Utilizing deep neural nets for an embedded ECG-based biometric authentication system  A Page, A Kulkarni, T Mohsenin
2015   A scalable and adaptable probabilistic model embedded in an electronic nose for intelligent sensor fusion  CT Tang, CM Huang, KT Tang, H Chen
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Deep Learning for Magnetic Resonance Imaging (MRI)

mri

Magnetic Resonance Imaging (MRI) can be used in many types of diagnosis e.g. cancer, alzheimer, cardiac and muscle/skeleton issues. This blog post has recent publications of Deep Learning applied to MRI (health-related) data, e.g. for segmentation, detection, demonising and classification.

MRI is described in Wikipedia as:

    Magnetic resonance imaging (MRI) is a medical imaging technique used in radiology to form pictures of the anatomy and the physiological processes of the body in both health and disease. MRI scanners use strong magnetic fields, radio waves, and field gradients to generate images of the organs in the body.

Best regards,
Amund Tveit

Year  Title Author
2017   Residual and Plain Convolutional Neural Networks for 3D Brain MRI Classification  S Korolev, A Safiullin, M Belyaev, Y Dodonova
2017   Automatic segmentation of the right ventricle from cardiac MRI using a learning‐based approach  MR Avendi, A Kheradvar, H Jafarkhani
2017   Learning a Variational Network for Reconstruction of Accelerated MRI Data  K Hammernik, T Klatzer, E Kobler, MP Recht
2017   A 2D/3D Convolutional Neural Network for Brain White Matter Lesion Detection in Multimodal MRI  L Roa
2017   On hierarchical brain tumor segmentation in MRI using fully convolutional neural networks: A preliminary study  S Pereira, A Oliveira, V Alves, CA Silva
2017   Classification of breast MRI lesions using small-size training sets: comparison of deep learning approaches  G Amit, R Ben
2017   A deep learning network for right ventricle segmentation in short-axis MRI  GN Luo, R An, KQ Wang, SY Dong, HG Zhang
2017   A novel left ventricular volumes prediction method based on deep learning network in cardiac MRI  GN Luo, GX Sun, KQ Wang, SY Dong, HG Zhang
2017   Classification of MRI data using Deep Learning and Gaussian Process-based Model Selection  H Bertrand, M Perrot, R Ardon, I Bloch
2017   Using Deep Learning to Segment Breast and Fibroglanduar Tissue in MRI Volumes  MU Dalmş, G Litjens, K Holland, A Setio, R Mann
2017   Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks  PF Christ, F Ettlinger, F Grün, MEA Elshaera, J Lipkova
2017   Automated Segmentation of Hyperintense Regions in FLAIR MRI Using Deep Learning  P Korfiatis, TL Kline, BJ Erickson
2017   Automatic segmentation of left ventricle in cardiac cine MRI images based on deep learning  T Zhou, I Icke, B Dogdas, S Parimal, S Sampath
2017   Deep artifact learning for compressed sensing and parallel MRI  D Lee, J Yoo, JC Ye
2017   Deep Generative Adversarial Networks for Compressed Sensing Automates MRI  M Mardani, E Gong, JY Cheng, S Vasanawala
2017   3D Motion Modeling and Reconstruction of Left Ventricle Wall in Cardiac MRI  D Yang, P Wu, C Tan, KM Pohl, L Axel, D Metaxas
2017   Estimation of the volume of the left ventricle from MRI images using deep neural networks  F Liao, X Chen, X Hu, S Song
2017   A fully automatic deep learning method for atrial scarring segmentation from late gadolinium-enhanced MRI images  G Yang, X Zhuang, H Khan, S Haldar, E Nyktari, X Ye
2017   Age estimation from brain MRI images using deep learning  TW Huang, HT Chen, R Fujimoto, K Ito, K Wu, K Sato
2017   Segmenting Atrial Fibrosis from Late Gadolinium-Enhanced Cardiac MRI by Deep-Learned Features with Stacked Sparse Auto-Encoders  S Haldar, E Nyktari, X Ye, G Slabaugh, T Wong
2017   Deep Residual Learning For Compressed Sensing Mri  D Lee, J Yoo, JC Ye
2017   Prostate cancer diagnosis using deep learning with 3D multiparametric MRI  S Liu, H Zheng, Y Feng, W Li
2017   Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions  Z Akkus, A Galimzianova, A Hoogi, DL Rubin
2016   Classification of Alzheimer’s Disease Structural MRI Data by Deep Learning Convolutional Neural Networks  S Sarraf, G Tofighi
2016   De-noising of Contrast-Enhanced MRI Sequences by an Ensemble of Expert Deep Neural Networks  A Benou, R Veksler, A Friedman, TR Raviv
2016   A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI  MR Avendi, A Kheradvar, H Jafarkhani
2016   Applying machine learning to automated segmentation of head and neck tumour volumes and organs at risk on radiotherapy planning CT and MRI scans  C Chu, J De Fauw, N Tomasev, BR Paredes, C Hughes
2016   A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI  PV Tran
2016   An Overview of Techniques for Cardiac Left Ventricle Segmentation on Short-Axis MRI  A Krasnobaev, A Sozykin
2016   Stacking denoising auto-encoders in a deep network to segment the brainstem on MRI in brain cancer patients: a clinical study  J Dolz, N Betrouni, M Quidet, D Kharroubi, HA Leroy
2016   Hough-CNN: Deep Learning for Segmentation of Deep Brain Regions in MRI and Ultrasound  F Milletari, SA Ahmadi, C Kroll, A Plate, V Rozanski
2016   Mental Disease Feature Extraction with MRI by 3D Convolutional Neural Network with Multi-channel Input  L Cao, Z Liu, X He, Y Cao, K Li
2016   Deep learning trends for focal brain pathology segmentation in MRI  M Havaei, N Guizard, H Larochelle, PM Jodoin
2016   Identification of Water and Fat Images in Dixon MRI Using Aggregated Patch-Based Convolutional Neural Networks  L Zhao, Y Zhan, D Nickel, M Fenchel, B Kiefer, XS Zhou
2016   Deep MRI brain extraction: A 3D convolutional neural network for skull stripping  J Kleesiek, G Urban, A Hubert, D Schwarz
2016   Active appearance model and deep learning for more accurate prostate segmentation on MRI  R Cheng, HR Roth, L Lu, S Wang, B Turkbey
2016   Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation  RPK Poudel, P Lamata, G Montana
2016   Deep learning predictions of survival based on MRI in amyotrophic lateral sclerosis  HK van der Burgh, R Schmidt, HJ Westeneng
2016   Semantic-Based Brain MRI Image Segmentation Using Convolutional Neural Network  Y Chou, DJ Lee, D Zhang
2016   Abstract WP41: Predicting Acute Ischemic Stroke Tissue Fate Using Deep Learning on Source Perfusion MRI  KC Ho, S El
2016   A new ASM framework for left ventricle segmentation exploring slice variability in cardiac MRI volumes  C Santiago, JC Nascimento, JS Marques
2015   Crohn’s disease segmentation from mri using learned image priors  D Mahapatra, P Schüffler, F Vos, JM Buhmann
2015   Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection  AG Chung, MJ Shafiee, D Kumar, F Khalvati
2015   Real-time Dynamic MRI Reconstruction using Stacked Denoising Autoencoder  A Majumdar
2015   q-Space Deep Learning for Twelve-Fold Shorter and Model-Free Diffusion MRI Scans  V Golkov, A Dosovitskiy, P Sämann, JI Sperl
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Deep Learning in combination with EEG electrical signals from the brain

eeg

EEG (Electroencephalography) is the measurement of electrical signals in the brain. It has long been used for medical purposes (e.g. diagnosis of epilepsy), and has in more recent years also been used in Brain Computer Interfaces (BCI) – note: if BCI is new to you don’t get overly excited about it, since these interfaces are still in my opinion quite premature. But they are definitely interesting in a longer term perspective .

This blog post gives an overview of recent research on Deep Learning in combination with EEG, e.g. r for classification, feature representation, diagnosis, safety (cognitive state of drivers) and hybrid methods (Computer Vision or Speech Recognition together with EEG and Deep Learning).

Best regards,

Amund Tveit

Diagnosis and Medicine

  1. Classification of Epileptic EEG Signals with Stacked Sparse Autoencoder Based on Deep Learning
    – Authors: Q Lin, S Ye, X Huang, S Li, M Zhang, Y Xue, Ws Chen (2016)
  2. Advanced use of EEG in drug development and personalized medicine
    – Authors: S Simpraga, R Alvarez (2016)
  3. Low-complexity algorithms for automatic detection of sleep stages and events for use in wearable EEG systems
    – Authors: Sa Imtiaz (2016)
  4. Predicting epileptic seizure from Electroencephalography (EEG) using hilbert huang transformation and neural network
    – Authors: Mo Rahman, Mn Karim (2015)
  5. Multi-Channel EEG based Sleep Stage Classification with Joint Collaborative Representation and Multiple Kernel Learning
    – Authors: J Shi, X Liu, Y Li, Q Zhang, Y Li, S Yin (2015)
  6. Superchords: decoding EEG signals in the millisecond range
    – Authors: R Normand, Ha Ferreira (2015)
  7. A novel motor imagery EEG recognition method based on deep learning
    – Authors: M Li, M Zhang, Y Sun (2016)

Brain Computer Interfaces

  1. Convolutional Networks for EEG Signal Classification in Non-Invasive Brain-Computer Interfaces
    – Authors: E Forney, C Anderson, W Gavin, P Davies (2016)
  2. Using Deep Learning for Human Computer Interface via Electroencephalography
    – Authors: S Redkar (2016)
  3. Deep learning EEG response representation for brain computer interface
    – Authors: L Jingwei, C Yin, Z Weidong (2015)

Cognition and Emotion

  1. Single-channel EEG-based mental fatigue detection based on deep belief network
    – Authors: P Li, W Jiang, F Su (2016)
  2. EEG-based prediction of driver’s cognitive performance by deep convolutional neural network
    – Authors: M Hajinoroozi, Z Mao, Tp Jung, Ct Lin, Y Huang (2016)
  3. EEG-based Driver Fatigue Detection using Hybrid Deep Generic Model
    – Authors: Pp San, Sh Ling, R Chai, Y Tran, A Craig, Ht Nguyen (2016)
  4. Mental State Recognition via Wearable EEG
    – Authors: P Bashivan, I Rish, S Heisig (2016)
  5. Single trial prediction of normal and excessive cognitive load through EEG feature fusion
    – Authors: P Bashivan, M Yeasin, Gm Bidelman (2016)
  6. Prediction of driver’s drowsy and alert states from EEG signals with deep learning
    – Authors: M Hajinoroozi, Z Mao, Y Huang (2016)
  7. Investigating Critical Frequency Bands and Channels for EEG-based Emotion Recognition with Deep Neural Networks
    – Authors: Wl Zheng, Bl Lu (2015)
  8. Deep learninig of EEG signals for emotion recognition
    – Authors: Y Gao, Hj Lee, Rm Mehmood (2015)
  9. EEG Based Emotion Identification Using Unsupervised Deep Feature Learning
    – Authors: X Li, P Zhang, D Song, G Yu, Y Hou, B Hu (2015)
  10. Feature extraction with deep belief networks for driver’s cognitive states prediction from EEG data
    – Authors: M Hajinoroozi, Tp Jung, Ct Lin, Y Huang (2015)
  11. Measurement Of Stress Intensity Using Eeg
    – Authors: V Tóth (2015)
  12. Revealing critical channels and frequency bands for emotion recognition from EEG with deep belief network
    – Authors: Wl Zheng, Ht Guo, Bl Lu (2015)
  13. Interpretable Deep Neural Networks for Single-Trial EEG Classification
    – Authors: I Sturm, S Bach, W Samek, Kr Müller (2016)

Hybrid methods – combining Deep Learning and EEG with other Deep Learning methods

  1. Improving Electroencephalography-Based Imagined Speech Recognition with a Simultaneous Video Data Stream
    – Authors: Sj Stolze (2016)
  2. A closed-loop system for rapid face retrieval by combining EEG and computer vision
    – Authors: Y Wang, L Jiang, B Cai, Y Wang, S Zhang, X Zheng (2015)
  3. Emotional Affect Estimation Using Video and EEG Data in Deep Neural Networks
    – Authors: A Frydenlund, F Rudzicz (2015)

Feature representation and Classification

  1. Improving EEG feature learning via synchronized facial video
    – Authors: X Li, X Jia, G Xun, A Zhang (2016)
  2. Decoding EEG and LFP signals using deep learning: heading TrueNorth
    – Authors: E Nurse, Bs Mashford, Aj Yepes, I Kiral (2016)
  3. Fast and efficient rejection of background waveforms in interictal EEG
    – Authors: E Bagheri, J Jin, J Dauwels, S Cash, Mb Westover (2016)
  4. Class-wise Deep Dictionaries for EEG Classification
    – Authors: P Khurana, A Majumdar, R Ward (2016)
  5. EEG-based affect states classification using Deep Belief Networks
    – Authors: H Xu, Kn Plataniotis (2016)
  6. Feature Learning from Incomplete EEG with Denoising Autoencoder
    – Authors: J Li, Z Struzik, L Zhang, A Cichocki (2014)
  7. Joint optimization of algorithmic suites for EEG analysis
    – Authors: E Santana, Aj Brockmeier, Jc Principe (2014)
  8. Deep Extreme Learning Machine and Its Application in EEG Classification
    – Authors: S Ding, N Zhang, X Xu, L Guo, J Zhang (2014)
  9. Deep Feature Learning for EEG Recordings
    – Authors: S Stober, A Sternin, Am Owen, Ja Grahn (2015)
  10. A Multichannel Deep Belief Network for the Classification of EEG Data
    – Authors: Am Al (2015)
  11. Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks
    – Authors: P Bashivan, I Rish, M Yeasin, N Codella (2015)
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