Groundbreaking research enabling applied AI solutions.
Our mission is to build practical and groundbreaking AI Research so companies and professionals can apply to their production systems. Our goal is to provide AI Solutions by implementing our algorithms, tools and technologies.
Our researchers and engineers are dedicated to working towards this goal and they contributes relentlessly towards building practical software and algorithms. We publish our research and present at leading conferences regularly but our differentiation is in applying it directly into production systems of industry verticals such as healthcare diagnostics, manufacturing operations and more.
Our researchers and engineers are dedicated to working towards this goal. To do so our team contributes relentlessly towards building practical software and algorithms.
adopt a unique strategy by applying it directly into industry verticals.
We believe that is the only way to walk the talk!
3rd October 2020
An energy efficient time-mode digit classification neural network implementation
This paper presents the design of an ultra-low energy neural network that uses time-mode signal processing). Handwritten digit classification using a single-layer artificial neural network (ANN) with a Softmin-based activation function is described as an implementation example. To realize time-mode operation, the presented design makes use of monostable multivibrator-based multiplying analogue-to-time converters, fixed-width pulse generators and basic digital gates. The time-mode digit classification ANN was designed in a standard CMOS 0.18 μm IC process and operates from a supply voltage of 0.6 V. The system operates on the MNIST database of handwritten digits with quantized neuron weights and has a classification accuracy of 88%, which is typical for single-layer ANNs, while dissipating 65.74 pJ per classification with a speed of 2.37 k classifications per second. This article is part of the theme issue ‘Harmonizing energy-autonomous computing and intelligence’.
1st October 2020
Artificial intelligence for dental image analysis: A Guide for Authors and Reviewers
Objectives: The number of studies employing artificial intelligence (AI), specifically machine and deep learning, for dental image analysis is growing fast. The majority of studies suffer from limitations in planning, conduct and reporting, resulting in low robustness and applicability. We here present a non-authorative guide for authors and reviewers to be applied, discussed and further developed.
Methods: Lending from existing reviews in other fields and founded on the principles of evidence-based research practice, a set of guidance items are presented, assisting future scientists, reviewers and editors in planning, conducting, reporting and evaluating studies on AI in dental image analysis. The items have been derived on a discussion basis within the ITU/WHO focus group “Artificial Intelligence for Health (AI4H)”, and the topic group “Dental diagnostics and digital dentistry” and should be rigorously appraised and adapted.
Results: Thirty-one items on planning, conducting and reporting studies were devised. These involve items on the study’s wider goal, focus, design and specific aims, data sampling and reporting, sample estimation, reference test construction, model parameters, training and evaluation, uncertainty and explainability, performance metrics and data partitions.
Conclusion: Scientists, reviewers and editors should consider this guide when planning, conducting, reporting and evaluating studies on AI for dental image analysis.
Clinical significance: Current studies on AI in dental image analysis show considerable weaknesses, hampering their replication and application. This non-authorative guide may assist scientists, reviewers and editors to overcome this issue and advance AI research in dentistry as well as facilitate a forward-debate on standards in this fields.
Authors: Falk Schwendicke1,2, Tarry Singh2,3, Jae-Hong Lee2,4, Robert Gaudin5, Joachim Krois1,2
September 21st, 2020
Seismic Facies Analysis using state of the art architecture: A deep domain adaptation approach
Deep neural networks (DNNs) are powerful tools that are able to learn accurately from large quantities of labeled input data. However DNNs cannot always generalize on test data sampled from different input distributions. We demonstrate the use of unsupervised Deep Domain Adaptation (DDA) in a DNN when no input labels are available and distribution shifts are observed in target domain (TD). Experiments are performed on seismic images of F3 block 3D dataset from offshore Netherlands as source domain (SD) and Penobscot 3D survey data from Canada as TD. Three geological classes fromSD and TD that have similar depositional environment and lithology are considered for the study. To approach the studywe developed a deep neural network architecture, EarthAdaptNet(EAN), specially designed for semantically segmenting the seismic images with a minimal amount of training data. More specifically, we use a transposed residual unit to replace the traditional dilated convolution in the decoder block. EarthAdaptNet shows promising results in comparison to baseline results and was able to achieve a pixel accuracy>84% and an accuracy of∼70% for the minority classes, an improvement on the pre-existing architectures.
March 30th, 2020
Feasibility Assessment of Artificial Intelligence in Breast Cancer Diagnostics
The issue of breast cancer is one which affects millions around the globe each year with those in developing regions facing disproportionately high rates of fatality. It is known the importance of early diagnosis, but with the relative scarcity and high workload of pathologists in these developing regions, it is vital to research how recent advances in artificial intelligence can serve to aid in the diagnosis of breast cancer. It has been shown that deep learning models already have the capacity for rapid diagnosis with many having the same performance as health-care professionals. In the field of breast cancer, there have been a variety of deep learning diagnostic models created; however, there are very few which have been made to diagnose FNAC results. Since breast cancer diagnosis through FNAC is well suited for developing countries due to its minimal cost and infrastructure requirements, developing a deep learning diagnostic model could be an invaluable tool to assist pathologists working in these regions.
There are a number of challenges associated with building the proposed diagnostic model, but the primary issue is the need for a large, balanced and properly labelled dataset. In the Materials and Methods section, a variety of techniques were discussed in detail to deal with these issues commonly faced with image classification tasks. The majority of these procedures such as regularization to prevent model overfitting, data augmentation to increase diversity of training examples, imputation of missing data entries, oversampling to correct for class imbalance, cost-sensitive learning to minimize false negative diagnosis, semi-supervised learning to handle images with missing labels, and transfer learning are fairly common procedures which many deep learning medical diagnostic models have employed to increase performance.
May 24th, 2019
Towards a Neurobiological Basis of Deep Learning
The human brain constantly executes myriad decisions every day – some trivial, many complex. Decision making and learning are fundamental to human survival, and our runaway success as a dominant species. Our brain continually decides by reflecting upon past experiences, while simultaneously acquiring new knowledge with every decision. Neuromodulators – acetylcholine (ACh), noradrenaline (NA), serotonin (5-HT), dopamine (DA), and histamine (HA) – reorganize the function of local neural networks neurons and shape the emergence of global brain states such as decision making and learning. The advent of artificial neural networks, which has benefited from the remarkable success of the brain’s ability to decide and learn, is attempting to transform human society through machine-based representations that mimic patterns of biological neural activity. For example, biologically inspired convolutional neural networks (CNNs) have shown promising performance in a variety of tasks including image recognition, classification and analysis. Recent studies have adopted a more biologically-realistic compartmental structure in the design of deep learning algorithms. Here, we review subcortical structures and neuromodulatory systems that regulate contextual decision making and learning in the brain, and outline proposals towards more efficient machine-based representations for neuromodulation-aware models of deep-learning. Taken together, a comprehensive review of existing findings on the role of neuromodulators in decision making and learning processes will be essential for evidence-driven, biologically-inspired deep learning models.
Coming at NM² Conference 24 – 26th May 2019.
Generative Model with Dynamic Linear Flow
Flow-based generative models are a family of exact log-likelihood models with tractable sampling and latent-variable inference, hence conceptually attractive for modelling complex distributions. However, flow-based models are limited by density estimation performance issues as compared to state-of-the-art autoregressive models. Autoregressive models, which also belong to the family of likelihood-based methods, however suffer from limited parallelizability. In this paper, we propose Dynamic Linear Flow (DLF), a new family of invertible transformations with partially autoregressive structure. Our method benefits from the efficient computation of flow-based methods and high density estimation performance of autoregressive methods. We demonstrate that the proposed DLF yields state-of-the-art performance on ImageNet 32×32 and 64×64 out of all flow-based methods,and is competitive with the best autoregressive model. Additionally, our model converges 10 times faster than Glow (Kingma and Dhariwal, 2018).
Intra-thalamic and Thalamocortical Connectivity: Potential Implication for Deep Learning
Utilizing Richard’s Curve for Controlling the Non-monotonicity of the Activation Function in Deep Neural Nets
Coming soon: DeepSwitch
Our DeepRRP (Deep Learning Remote Residency) Program is currently in beta open for AI Researchers who are already conducting some form of research in Machine Learning, Deep Learning or Artificial Intelligence.
For more information on our selection criteria, please click the enroll button below.