New Publications in Deep Learning Publication Navigator

Long overdue update of new publications in Deep Learning Publication Navigator (ai.amundtveit.com) – for now the easiest way to discover new publications is probably to convert screenshots (number of papers) per category in the before and after update screenshots below.

Examples of keywords (from publication title) with (several) new Deep Learning publications are:

  1. 3D
  2. Acoustic
  3. Active learning
  4. Adaptive
  5. Adversarial (123 new papers since last update, due to significant activity in GAN Research)
  6. Alzheimer’s (22 new papers related to a disease that cost more than a quarter trillion US$ annually to treat in the USA)
  7. Anomaly detection
  8. Autoencoders
  9. Bayesian
  10. Biomedical
  11. Chinese
  12. Clinical
  13. Collaborative filtering (e.g. for recommender systems)
  14. Dataset
  15. EEG (electric brain signals)
  16. Ensemble
  17. +++++ (many more!)

If you have feature ideas or other requests for Deep Learning Publication Navigator, feel free to reach out.

Best regards,

Amund Tveit

After update (with new papers):

Before update (without new papers):

 

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Creative AI on the iPhone (with GAN) and Dynamic Loading of CoreML models

Zedge summer interns developed a very cool app using ARKit and CoreML (on iOS11). As parts of their journey the published 2 blog posts on the Zedge corporate web site related to:

  1. How to develop and run Generative Adversarial Networks (GAN) for Creative AI on the iPhone using Apple’s CoreML tools, check out their blog post about it.
  2. Deep Learning models (e.g. for GAN) can take a lot of space on a mobile device (tens of Megabytes to perhaps even Gigabytes), in order to keep initial app download size relatively low it can be useful to dynamically load only the models you need. Check out their blog post about various approaches for hotswapping CoreML models.

Best regards,

Amund Tveit 

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Deep Learning in Energy Production

This blog post has recent publications about use of Deep Learning in Energy Production context (wind, gas and oil), e.g. wind power prediction, turbine risk assessment, reservoir discovery and price forecasting.

Best regards,

Amund Tveit

Wind

Year  Title Author
2017 Short-term Wind Energy Prediction Algorithm Based on SAGA-DBNs  W Fei, WU Zhong
2017 Wind Power Prediction using Deep Neural Network based Meta Regression and Transfer Learning  AS Qureshi, A Khan, A Zameer, A Usman
2017 Wind Turbine Failure Risk Assessment Model Based on DBN  C Fei, F Zhongguang
2017 The optimization of wind power interval forecast  X Yu, H Zang
2016 Deep Learning for Wind Speed Forecasting in Northeastern Region of Brazil  AT Sergio, TB Ludermir
2016 A very short term wind power prediction approach based on Multilayer Restricted Boltzmann Machine  X Peng, L Xiong, J Wen, Y Xu, W Fan, S Feng, B Wang
2016 Short-term prediction of wind power based on deep Long Short-Term Memory  Q Xiaoyun, K Xiaoning, Z Chao, J Shuai, M Xiuda
2016 Deep belief network based deterministic and probabilistic wind speed forecasting approach  HZ Wang, GB Wang, GQ Li, JC Peng, YT Liu
2016 A hybrid wind power prediction method  Y Tao, H Chen
2016 Deep learning based ensemble approach for probabilistic wind power forecasting  H Wang, G Li, G Wang, J Peng, H Jiang, Y Liu
2016 A hybrid wind power forecasting model based on data mining and wavelets analysis  R Azimi, M Ghofrani, M Ghayekhloo
2016 ELM Based Representational Learning for Fault Diagnosis of Wind Turbine Equipment  Z Yang, X Wang, PK Wong, J Zhong
2015 Deep Neural Networks for Wind Energy Prediction  D Díaz, A Torres, JR Dorronsoro
2015 Predictive Deep Boltzmann Machine for Multiperiod Wind Speed Forecasting  CY Zhang, CLP Chen, M Gan, L Chen
2015 Resilient Propagation for Multivariate Wind Power Prediction  J Stubbemann, NA Treiber, O Kramer
2015 Transfer learning for short-term wind speed prediction with deep neural networks  Q Hu, R Zhang, Y Zhou
2014 Wind Power Prediction and Pattern Feature Based on Deep Learning Method  Y Tao, H Chen, C Qiu

Gas

Year  Title Author
2017   Sample Document–Inversion Of The Permeability Of A Tight Gas Reservoir With The Combination Of A Deep Boltzmann Kernel …  L Zhu, C Zhang, Y Wei, X Zhou, Y Huang, C Zhang
2017   Deep Learning: Chance and Challenge for Deep Gas Reservoir Identification  C Junxing, W Shikai
2016   Finite-sensor fault-diagnosis simulation study of gas turbine engine using information entropy and deep belief networks  D Feng, M Xiao, Y Liu, H Song, Z Yang, Z Hu
2015   On Accurate and Reliable Anomaly Detection for Gas Turbine Combustors: A Deep Learning Approach  W Yan, L Yu
2015   A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas and Water Grids: Analysis and Experiments.  M Fagiani, S Squartini, L Gabrielli, S Spinsante
2015   Short-term load forecasting for smart water and gas grids: A comparative evaluation  M Fagiani, S Squartini, R Bonfigli, F Piazza
2015   The early-warning model of equipment chain in gas pipeline based on DNN-HMM  J Qiu, W Liang, X Yu, M Zhang, L Zhang

Oil

Year  Title Author
2017   Development of a New Correlation for Bubble Point Pressure in Oil Reservoirs Using Artificial Intelligent Technique  S Elkatatny, M Mahmoud
2017   A deep learning ensemble approach for crude oil price forecasting  Y Zhao, J Li, L Yu
2016   Automatic Detection and Classification of Oil Tanks in Optical Satellite Images Based on Convolutional Neural Network  Q Wang, J Zhang, X Hu, Y Wang
2015   A Hierarchical Oil Tank Detector With Deep Surrounding Features for High-Resolution Optical Satellite Imagery  L Zhang, Z Shi, J Wu
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Traffic Sign Detection with Convolutional Neural Networks

Making Self-driving cars work requires several technologies and methods to pull in the same direction (e.g. Radar/Lidar, Camera, Control Theory and Deep Learning). The online available Self-Driving Car Nanodegree from Udacity (divided into 3 terms) is probably the best way to learn more about the topic (see [Term 1], [Term 2] and [Term 3] for more details about each term), the coolest part is that you actually can run your code on an actual self-driving car towards the end of term 3 (I am currently in the middle of term 1 – highly recommended course!).

Note: before taking this course I recommend taking Udacity’s Deep Learning Nanodegree Foundations since most (term 1) projects requires some hands-on experience with Deep Learning.

Traffic Sign Detection with Convolutional Neural Networks

This blog post is a writeup of my (non-perfect) approach for German traffic sign detection (a project in the course) with Convolutional Neural networks (in TensorFlow) – a variant of LeNet with Dropout and (the new) SELU – Self-Normalizing Neural Networks. The effect of SELU was primarily that it quickly gained classification accuracy (even in first epoch), but didn’t lead to higher accuracy than using batch-normalisation + RELU in the end. (Details at: github.com/atveit/TrafficSignClassification). Data Augmentation in particular and perhaps a deeper network could have improved the performance I believe.

For other approaches (e.g. R-CNN and cascaded deep networks) see the blog post: Deep Learning for Vehicle Detection and Recognition.

UPDATE – 2017-July-15:

If you thought Traffic Sign Detection from modern cars was an entire solved problem, think again:

TeslaTrafficSign

 

Best regards,

Amund Tveit

1. Basic summary of the German Traffic Sign Data set.

I used numpy shape to calculate summary statistics of the traffic signs data set:

  • The size of training set is ? 34799
  • The size of the validation set is ? 4410
  • The size of test set is ? 12630
  • The shape of a traffic sign image is ? 32x32x3 (3 color channels, RGB)
  • The number of unique classes/labels in the data set is ? 43

2. Visualization of the train, validation and test dataset.

Here is an exploratory visualization of the data set. It is a bar chart showing how the normalized distribution of data for the 43 traffic signs. The key takeaway is that the relative number of data points varies quite a bit between each class, e.g. from around 6.5% (e.g. class 1) to 0.05% (e.g. class 37), i.e. a factor of at least 12 difference (6.5% / 0.05%), this can potentially impact classification performance.

alt text

3 Design of Architecture

3.1 Preprocessing of images

Did no grayscale conversion or other conversion of train/test/validation images (they were preprocessed). For the images from the Internet they were read from using PIL and converted to RGB (from RBGA), resized to 32×32 and converted to numpy array before normalization.

All images were normalized pixels in each color channel (RGB – 3 channels with values between 0 to 255) to be between -0.5 to 0.5 by dividing by (128-value)/255. Did no data augmentation.

Here are sample images from the training set

alt text

3.2 Model Architecture

Given the relatively low resolution of Images I started with Lenet example provided in lectures, but to improve training I added Dropout (in early layers) with RELU rectifier functions. Recently read about self-normalizing rectifier function – SELU – so decided to try that instead of RELU. It gave no better end result after many epochs, but trained much faster (got > 90% in one epoch), so kept SELU in the original. For more information about SELU check out the paper Self-Normalizing Neural Networks from Johannes Kepler University in Linz, Austria.

My final model consisted of the following layers:

Layer Description
Input 32x32x3 RGB image
Convolution 5×5 1×1 stride, valid padding, outputs 28x28x6
Dropout keep_prob = 0.9
SELU
Max Pooling 2×2 stride, outputs 14x14x6
Convolution 5×5 1×1 stride, valid padding, outputs 10x10x16
SELU
Dropout keep_prob = 0.9
Max Pooling 2×2 stride, outputs 5x5x16
Flatten output dimension 400
Fully connected output dimension 120
SELU
Fully connected output dimension 84
SELU
Fully connected output dimension 84
SELU
Fully connected output dimension 43

3.3 Training of Model

To train the model, I used an Adam optimizer with learning rate of 0.002, 20 epochs (converged fast with SELU) and batch size of 256 (ran on GTX 1070 with 8GB GPU RAM)

3.4 Approach to find solution and getting accuracy > 0.93

Adding dropout to Lenet improved test accuracy and SELU improved training speed. The originally partitioned data sets were quite unbalanced (when plotting), so reading all data, shuffling and creating training/validation/test set also helped. I thought about using Keras and fine tuning a pretrained model (e.g. inception 3), but it could be that a big model on such small images could lead to overfitting (not entirely sure about that though), and reducing input size might lead to long training time (looks like fine tuning is best when you have the same input size, but changing the output classes)

My final model results were:

  • validation set accuracy of 0.976 (between 0.975-0.982)
  • test set accuracy of 0.975

If an iterative approach was chosen:

  • What was the first architecture that was tried and why was it chosen?

Started with Lenet and incrementally added dropout and then several SELU layers.. Also added one fully connected layer more.

  • What were some problems with the initial architecture?

No, but not great results before adding dropout (to avoid overfitting)

  • Which parameters were tuned? How were they adjusted and why?

Tried several combinations learning rates. Could reduce epochs after adding SELU. Used same dropout keep rate.

Since the difference between validation accuracy and test accuracy is very low the model seems to be working well. The loss is also quite low (0.02), so little to gain most likely – at least without changing the model a lot.

4 Test a Model on New Images

4.1. Choose five German traffic signs found on the web

Here are five German traffic signs that I found on the web:

alt text

In the first pick of images I didn’t check that the signs actually were among the the 43 classes the model was built for, and that was actually not the case, i.e. making it impossible to classify correctly. But got interesting results (regarding finding similar signs) for the wrongly classified ones, so replaced only 2 of them with sign images that actually was covered in the model, i.e. making it still impossible to classify 3 of them.

Here are the results of the prediction:

Image Prediction
Priority road Priority road
Side road Speed limit (50km/h)
Adult and child on road Turn left ahead
Two way traffic ahead Beware of ice/snow
Speed limit (60km/h) Speed limit (60km/h)

The model was able to correctly guess 2 of the 5 traffic signs, which gives an accuracy of 40%. For the other ones it can`t classify correctly, but the 2nd prediction for sign 3 – “adult and child on road” – is interesting since it suggests “Go straight or right” – which is quite visually similar (if you blur the innermost of each sign you will get almost the same image).

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Deep Learning for Protein(omics)

This blog post has recent publications related to Deep Learning for proteinomics (the study of proteins). Proteins are a set of molecules in the human (and animal) bodies (probably best known for their role related to muscle mass and in DNA replication).

Wikipedia describes proteins as:

    Proteins (/ˈproʊˌtiːnz/ or /ˈproʊti.ᵻnz/) are large biomolecules, or macromolecules, consisting of one or more long chains of amino acid residues. Proteins perform a vast array of functions within organisms, including catalysing metabolic reactions, DNA replication, responding to stimuli, and transporting molecules from one location to another. Proteins differ from one another primarily in their sequence of amino acids, which is dictated by the nucleotide sequence of their genes, and which usually results in protein folding into a specific three-dimensional structure that determines its activity.

Best regards,
Amund Tveit (WeChat ID: AmundTveit)

Year  Title Author
2017   Deep Recurrent Neural Network for Protein Function Prediction from Sequence  XL Liu
2017   Sequence-based prediction of protein protein interaction using a deep-learning algorithm  T Sun, B Zhou, L Lai, J Pei
2017   Protein Model Quality Assessment: A Machine Learning Approach  K Uziela
2017   Deep convolutional neural networks for detecting secondary structures in protein density maps from cryo-electron microscopy  R Li, D Si, T Zeng, S Ji, J He
2017   Towards recognition of protein function based on its structure using deep convolutional networks  A Tavanaei, AS Maida, A Kaniymattam
2017   Improved protein model quality prediction by changing the target function  K Uziela, D Menendez Hurtado, N Shu, B Wallner
2017   A Novel Model Based On Fcm-Lm Algorithm For Prediction Of Protein Folding Rate  L Liu, M Ma, J Cui
2017   EPSILON-CP: using deep learning to combine information from multiple sources for protein contact prediction  K Stahl, M Schneider, O Brock
2017   Prediction of protein function using a deep convolutional neural network ensemble  EI Zacharaki
2017   Protein Function Prediction using Deep Restricted Boltzmann Machines  X Zou, G Wang, G Yu
2017   Next-Step Conditioned Deep Convolutional Neural Networks Improve Protein Secondary Structure Prediction  A Busia, N Jaitly
2017   Predicting membrane protein contacts from non-membrane proteins by deep transfer learning  Z Li, S Wang, Y Yu, J Xu
2017   A Template-Based Protein Structure Reconstruction Method Using Deep Autoencoder Learning  H Li, Q Lyu, J Cheng
2017   DNpro: A Deep Learning Network Approach to Predicting Protein Stability Changes Induced by Single-Site Mutations  X Zhou, J Cheng
2017   Computational Methods for the Prediction of Drug-Target Interactions from Drug Fingerprints and Protein Sequences by Stacked Auto-Encoder Deep Neural Network  L Wang, ZH You, X Chen, SX Xia, F Liu, X Yan, Y Zhou
2017   Multi-task Deep Neural Networks in Automated Protein Function Prediction  AS Rifaioglu, T Doğan, MJ Martin, R Cetin
2016   AUC-Maximized Deep Convolutional Neural Fields for Protein Sequence Labeling  S Wang, S Sun, J Xu
2016   Evaluation of Protein Structural Models Using Random Forests  R Cao, T Jo, J Cheng
2016   A Protein Domain and Family Based Approach to Rare Variant Association Analysis  TG Richardson, HA Shihab, MA Rivas, MI McCarthy
2016   Protein Sequencing And Neural Network Classification Methods  V Indarni, SK Terala, PV Bhushan, MR Ireddy
2016   Accurate prediction of docked protein structure similarity using neural networks and restricted Boltzmann machines  R Farhoodi, B Akbal
2016   Identification of thermostabilizing mutations for a membrane protein whose three‐dimensional structure is unknown  Y Kajiwara, S Yasuda, Y Takamuku, T Murata
2016   Identification of Genetic Sequences Recognized by Human SC35 Protein Using Artificial Neural Networks: A Deep Learning Approach  AJ Luke, S Fergione
2016   MUST-CNN: A MUltilayer Shift-and-sTitch Deep Convolutional Architecture for Sequence-based Protein Structure Prediction  Z Lin, Y Qi
2016   Protein Secondary Structure Prediction Using Deep Multi-scale Convolutional Neural Networks and Next-Step Conditioning  A Busia, J Collins, N Jaitly
2016   A computational framework for disease grading using protein signatures  E Zerhouni, B Prisacari, Q Zhong, P Wild, M Gabrani
2016   ProtPOS: a python package for the prediction of protein preferred orientation on a surface  JCF Ngai, PI Mak, SWI Siu
2016   DeepQA: Improving the estimation of single protein model quality with deep belief networks  R Cao, D Bhattacharya, J Hou, J Cheng
2016   Protein contact prediction from amino acid co-evolution using convolutional networks for graph-valued images  V Golkov, MJ Skwark, A Golkov, A Dosovitskiy, T Brox
2016   Protein Secondary Structure Prediction by using Deep Learning Method  Y Wang, H Mao, Z Yi
2016   On the importance of composite protein multiple ligand interactions in protein pockets  S Tonddast‐Navaei, B Srinivasan, J Skolnick
2016   Protein function in precision medicine: deep understanding with machine learning  B Rost, P Radivojac, Y Bromberg
2016   Protein Residue-Residue Contact Prediction Using Stacked Denoising Autoencoders  IV Luttrell, J Bailey
2016   Protein Residue Contacts and Prediction Methods  B Adhikari, J Cheng
2016   RaptorX-Property: a web server for protein structure property prediction.  S Wang, W Li, S Liu, J Xu
2016   AUCpreD: proteome-level protein disorder prediction by AUC-maximized deep convolutional neural fields  S Wang, J Ma, J Xu
2016   Benchmarking Deep Networks for Predicting Residue-Specific Quality of Individual Protein Models in CASP11  T Liu, Y Wang, J Eickholt, Z Wang
2015   Theory, Methods, and Applications of Coevolution in Protein Contact Prediction  J Ma, S Wang
2015   A topological approach for protein classification  Z Cang, L Mu, K Wu, K Opron, K Xia, GW Wei
2015   Application of Learning to Rank to protein remote homology detection  B Liu, J Chen, X Wang
2015   Improving Protein Fold Recognition by Deep Learning Networks  T Jo, J Hou, J Eickholt, J Cheng
2015   Proteins, physics and probability kinematics: a Bayesian formulation of the protein folding problem  T Hamelryck, W Boomsma, J Ferkinghoff
2015   DeepCNF-D: Predicting Protein Order/Disorder Regions by Weighted Deep Convolutional Neural Fields  S Wang, S Weng, J Ma, Q Tang
2015   A deep learning framework for modeling structural features of RNA-binding protein targets  S Zhang, J Zhou, H Hu, H Gong, L Chen, C Cheng
2015   A serum protein test for improved prognostic stratification of patients with myelodysplastic syndrome (MDS)  J Roder, J Löffler
2015   An Overview of Practical Applications of Protein Disorder Prediction and Drive for Faster, More Accurate Predictions  X Deng, J Gumm, S Karki, J Eickholt, J Cheng
2015   A panel of mass spectrometry based serum protein tests for predicting graft-versus-host disease (GvHD) and its severity  H Roder, AC Hoffmann, J Roder, M Koldehoff
2015   Learning Deep Architectures for Protein Structure Prediction  K Baek
2015   Protein secondary structure prediction using deep convolutional neural fields  S Wang, J Peng, J Ma, J Xu
2015   Protein sequence labelling by AUC-maximized Deep Convolutional Neural Fields  S Wang, J Ma, S Sun, J Xu
2015   Fast loop modeling for protein structures  J Zhang, S Nguyen, Y Shang, D Xu, I Kosztin
2015   Introducing Students to Protein Analysis Techniques: Separation and Comparative Analysis of Gluten Proteins in Various Wheat Strains  AL Pirinelli, JC Trinidad, NLB Pohl
2014   Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network  J Lyons, A Dehzangi, R Heffernan, A Sharma
2014   Improved contact predictions using the recognition of protein like contact patterns.  MJ Skwark, D Raimondi, M Michel, A Elofsson
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Deep Learning for Embedded Systems

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)

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 for Acoustic Modelling

This blog post has an overview papers related to acoustic modelling primarily for speech recognition but also speech generation (synthesis). See also ai.amundtveit.com/keyword/acoustic for a broader set of (at the time of writing 73) recent Deep Learning papers related to acoustics for speech recognition and other applications of acoustics.

Acoustic Modelling is described in Wikipedia as: “An acoustic model is used in Automatic Speech Recognition to represent the relationship between an audio signal and the phonemes or other linguistic units that make up speech. The model is learned from a set of audio recordings and their corresponding transcripts”. 

Blog Post Illustration Photo Source: Professor Mark Gales‘ (University of Cambridge) 2009 presentation Acoustic Modelling for Speech Recognition: Hidden Markov Models and Beyond?

Best regards,

Amund Tveit

Year  Title Author
2017   Investigation on acoustic modeling with different phoneme set for continuous Lhasa Tibetan recognition based on DNN method  H Wang, K Khyuru, J Li, G Li, J Dang, L Huang
2017   Personalized Acoustic Modeling By Weakly Supervised Multi-Task Deep Learning Using Acoustic Tokens  CK Wei, CT Chung, HY Lee, LS Lee
2017   I-vector estimation as auxiliary task for multi-task learning based acoustic modeling for automatic speech recognition  G Pironkov, S Dupont, T Dutoit
2016   Graph-based Semi-Supervised Learning in Acoustic Modeling for Automatic Speech Recognition  Y Liu
2016   A Comprehensive Study of Deep Bidirectional LSTM RNNs for Acoustic Modeling in Speech Recognition  A Zeyer, P Doetsch, P Voigtlaender, R Schlüter, H Ney
2016   Improvements in IITG Assamese Spoken Query System: Background Noise Suppression and Alternate Acoustic Modeling  S Shahnawazuddin, D Thotappa, A Dey, S Imani
2016   DNN-Based Acoustic Modeling for Russian Speech Recognition Using Kaldi  I Kipyatkova, A Karpov
2015   Doubly Hierarchical Dirichlet Process Hmm For Acoustic Modeling  AHHN Torbati, J Picone
2015   Deep Learning for Acoustic Modeling in Parametric Speech Generation: A systematic review of existing techniques and future trends  ZH Ling, SY Kang, H Zen, A Senior, M Schuster
2015   Acoustic Modeling In Statistical Parametric Speech Synthesis–From Hmm To Lstm-Rnn  H Zen
2015   Acoustic Modeling of Bangla Words using Deep Belief Network  M Ahmed, PC Shill, K Islam, MAH Akhand
2015   Unified Acoustic Modeling using Deep Conditional Random Fields  Y Hifny
2015   Exploiting Low-Dimensional Structures To Enhance Dnn Based Acoustic Modeling In Speech Recognition  P Dighe, G Luyet, A Asaei, H Bourlard
2015   Ensemble Acoustic Modeling for CD-DNN-HMM Using Random Forests of Phonetic Decision Trees  T Zhao, Y Zhao, X Chen
2015   Deep Neural Networks for Acoustic Modeling  V from Embeds, G Hinton, L Deng, D Yu, G Dahl
2015   Integrating Articulatory Data in Deep Neural Network-based Acoustic Modeling  L Badino, C Canevari, L Fadiga, G Metta
2015   Deep learning in acoustic modeling for Automatic Speech Recognition and Understanding-an overview  I Gavat, D Militaru
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