Methods for Automated Neuron Image Analysis

Knowledge of neuronal cell morphology is essential for performing specialized analyses in the endeavor to understand neuron behavior and unravel the underlying principles of brain function. Neurons can be captured with a high level of detail using modern microscopes, but many neuroscientific studies require a more explicit and accessible representation than offered by the resulting images, underscoring the need for digital reconstruction of neuronal morphology from the images into a tree-like graph structure. This thesis proposes new computational methods for automated detection and reconstruction of neurons from fluorescence microscopy images. Specifically, the successive chapters describe and evaluate original solutions to problems such as the detection of landmarks (critical points) of the neuronal tree, complete tracing and reconstruction of the tree, and the detection of regions containing neurons in high-content screens

HMC-Based Accelerator Design For Compressed Deep Neural Networks

Deep Neural Networks (DNNs) offer remarkable performance of classifications and regressions in many high dimensional problems and have been widely utilized in real-word cognitive applications. In DNN applications, high computational cost of DNNs greatly hinder their deployment in resource-constrained applications, real-time systems and edge computing platforms. Moreover, energy consumption and performance cost of moving data between memory hierarchy and computational units are higher than that of the computation itself. To overcome the memory bottleneck, data locality and temporal data reuse are improved in accelerator design. In an attempt to further improve data locality, memory manufacturers have invented 3D-stacked memory where multiple layers of memory arrays are stacked on top of each other. Inherited from the concept of Process-In-Memory (PIM), some 3D-stacked memory architectures also include a logic layer that can integrate general-purpose computational logic directly within main memory to take advantages of high internal bandwidth during computation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compression and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling controller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation. In this dissertation, we are going to investigate hardware/software co-design for neural network accelerator. Specifically, we introduce a two-phase filter pruning framework for model compres- sion and an accelerator tailored for efficient DNN execution on HMC, which can dynamically offload the primitives and functions to PIM logic layer through a latency-aware scheduling con- troller. In our compression framework, we formulate filter pruning process as an optimization problem and propose a filter selection criterion measured by conditional entropy. The key idea of our proposed approach is to establish a quantitative connection between filters and model accuracy. We define the connection as conditional entropy over filters in a convolutional layer, i.e., distribution of entropy conditioned on network loss. Based on the definition, different pruning efficiencies of global and layer-wise pruning strategies are compared, and two-phase pruning method is proposed. The proposed pruning method can achieve a reduction of 88% filters and 46% inference time reduction on VGG16 within 2% accuracy degradation

On Improving Generalization of CNN-Based Image Classification with Delineation Maps Using the CORF Push-Pull Inhibition Operator

Deployed image classification pipelines are typically dependent on the images captured in real-world environments. This means that images might be affected by different sources of perturbations (e.g. sensor noise in low-light environments). The main challenge arises by the fact that image quality directly impacts the reliability and consistency of classification tasks. This challenge has, hence, attracted wide interest within the computer vision communities. We propose a transformation step that attempts to enhance the generalization ability of CNN models in the presence of unseen noise in the test set. Concretely, the delineation maps of given images are determined using the CORF push-pull inhibition operator. Such an operation transforms an input image into a space that is more robust to noise before being processed by a CNN. We evaluated our approach on the Fashion MNIST data set with an AlexNet model. It turned out that the proposed CORF-augmented pipeline achieved comparable results on noise-free images to those of a conventional AlexNet classification model without CORF delineation maps, but it consistently achieved significantly superior performance on test images perturbed with different levels of Gaussian and uniform noise

Convolutional Neural Network in Pattern Recognition

Since convolutional neural network (CNN) was first implemented by Yann LeCun et al. in 1989, CNN and its variants have been widely implemented to numerous topics of pattern recognition, and have been considered as the most crucial techniques in the field of artificial intelligence and computer vision. This dissertation not only demonstrates the implementation aspect of CNN, but also lays emphasis on the methodology of neural network (NN) based classifier. As known to many, one general pipeline of NN-based classifier can be recognized as three stages: pre-processing, inference by models, and post-processing. To demonstrate the importance of pre-processing techniques, this dissertation presents how to model actual problems in medical pattern recognition and image processing by introducing conceptual abstraction and fuzzification. In particular, a transformer on the basis of self-attention mechanism, namely beat-rhythm transformer, greatly benefits from correct R-peak detection results and conceptual fuzzification. Recently proposed self-attention mechanism has been proven to be the top performer in the fields of computer vision and natural language processing. In spite of the pleasant accuracy and precision it has gained, it usually consumes huge computational resources to perform self-attention. Therefore, realtime global attention network is proposed to make a better trade-off between efficiency and performance for the task of image segmentation. To illustrate more on the stage of inference, we also propose models to detect polyps via Faster R-CNN - one of the most popular CNN-based 2D detectors, as well as a 3D object detection pipeline for regressing 3D bounding boxes from LiDAR points and stereo image pairs powered by CNN. The goal for post-processing stage is to refine artifacts inferred by models. For the semantic segmentation task, the dilated continuous random field is proposed to be better fitted to CNN-based models than the widely implemented fully-connected continuous random field. Proposed approaches can be further integrated into a reinforcement learning architecture for robotics

Motor imagery-based brain-computer interface by implementing a frequency band selection

Les interfícies cervell-ordinador basades en imaginacions motores (MI-BCI) són una promesa per a revolucionar la manera com els humans interactuen amb les màquines o el programari, realitzant accions només amb el pensament. Els pacients que pateixen discapacitats de moviment crítiques, com l'esclerosi lateral amiotròfica (ALS) o la tetraplegia, podrien utilitzar aquesta tecnologia per controlar una cadira de rodes, pròtesis robòtiques o qualsevol altre dispositiu que els permeti interactuar de manera independent amb el seu entorn. L'objectiu d'aquest projecte és ajudar les comunitats afectades per aquests trastorns amb el desenvolupament d'un mètode que sigui capaç de detectar, amb la màxima precisió possible, la intenció d'executar moviments (sense que es produeixin) en les extremitats superiors del cos. Això es farà mitjançant senyals adquirits amb un electroencefalograma (EEG), el seu condicionament i processament, i la seva posterior classificació amb models d'intel·ligència artificial. A més, es dissenyarà un filtre de senyal digital per mantenir les bandes de freqüència més característiques de cada individu i augmentar significativament l’exactitud del sistema. Després d'extreure les característiques estadístiques, freqüencials i espacials més discriminatòries, va ser possible obtenir una exactitud del 88% en les dades de validació a l'hora de detectar si un participant estava imaginant un moviment de la mà esquerra o de la dreta. A més, es va utilitzar una xarxa neuronal convolucional (CNN) per distingir si el participant estava imaginant un moviment o no, la qual cosa va aconseguir una exactitud del 78% i una precisió del 90%. Aquests resultats es verificaran mitjançant la implementació d'una simulació en temps real amb l'ús d'un braç robòtic.Las interfaces cerebro-computadora basadas en imaginaciones motoras (MI-BCI) son una promesa para revolucionar la forma en que los humanos interactúan con las máquinas o el software, realizando acciones con tan solo pensar en ellas. Los pacientes que sufren discapacidades críticas del movimiento, como la esclerosis lateral amiotrófica (ALS) o la tetraplejia, podrían usar esta tecnología para controlar una silla de ruedas, prótesis robóticas o cualquier otro dispositivo que les permita interactuar de manera independiente con su entorno. El objetivo de este proyecto es ayudar a las comunidades afectadas por estos trastornos con el desarrollo de un método que sea capaz de detectar, con la mayor precisión posible, la intención de ejecutar movimientos (sin que se produzcan) en las extremidades superiores del cuerpo. Esto se hará mediante señales adquiridas con un electroencefalograma (EEG), su acondicionamiento y procesamiento, y su posterior clasificación con modelos de inteligencia artificial. Además, se diseñará un filtro de señal digital para mantener las bandas de frecuencia más características de cada individuo y aumentar significativamente la exactitud del sistema. Después de extraer características estadísticas, frecuenciales y espaciales discriminatorias, fue posible obtener una exactitud del 88% en los datos de validación a la hora de detectar si un participante estaba imaginando un movimiento con la mano izquierda o con la derecha. Además, se utilizó una red neural convolucional (CNN) para distinguir si el participante estaba imaginando un movimiento o no, lo que logró un 78% de exactitud y un 90% de precisión. Estos resultados se verificarán implementando una simulación en tiempo real con el uso de un brazo robótico.Motor Imagery-based Brain-Computer Interfaces (MI-BCI) are a promise to revolutionize the way humans interact with machinery or software, performing actions by just thinking about them. Patients suffering from critical movement disabilities, such as amyotrophic lateral sclerosis (ALS) or tetraplegia, could use this technology to control a wheelchair, robotic prostheses, or any other device that could let them interact independently with their surroundings. The focus of this project is to aid communities affected by these disorders with the development of a method that is capable of detecting, as accurately as possible, the intention to execute movements (without them occurring) in the upper extremities of the body. This will be done through signals acquired with an electroencephalogram (EEG), their conditioning and processing, and their subsequent classification with artificial intelligence models. In addition, a digital signal filter will be designed to keep the most characteristic frequency bands of each individual and increase accuracy significantly. After extracting discriminative statistical, frequential, and spatial features, it was possible to obtain an 88% accuracy on validation data when it came to detecting whether a participant was imagining a left-hand or a right-hand movement. Furthermore, a Convolutional Neural Network (CNN) was used to distinguish if the participant was imagining a movement or not, which achieved a 78% accuracy and a 90% precision. These results will be verified by implementing a real-time simulation with the usage of a robotic arm