The Right Direction Needed to Develop White-Box Deep Learning in Radiology, Pathology, and Ophthalmology: A Short Review

The popularity of deep learning (DL) in the machine learning community has been dramatically increasing since 2012. The theoretical foundations of DL are well-rooted in the classical neural network (NN). Rule extraction is not a new concept, but was originally devised for a shallow NN. For about the past 30 years, extensive efforts have been made by many researchers to resolve the “black box” problem of trained shallow NNs using rule extraction technology. A rule extraction technology that is well-balanced between accuracy and interpretability has recently been proposed for shallow NNs as a promising means to address this black box problem. Recently, we have been confronting a “new black box” problem caused by highly complex deep NNs (DNNs) generated by DL. In this paper, we first review four rule extraction approaches to resolve the black box problem of DNNs trained by DL in computer vision. Next, we discuss the fundamental limitations and criticisms of current DL approaches in radiology, pathology, and ophthalmology from the black box point of view. We also review the conversion methods from DNNs to decision trees and point out their limitations. Furthermore, we describe a transparent approach for resolving the black box problem of DNNs trained by a deep belief network. Finally, we provide a brief description to realize the transparency of DNNs generated by a convolutional NN and discuss a practical way to realize the transparency of DL in radiology, pathology, and ophthalmology

Salient Arithmetic Data Extraction from Brain Activity via an Improved Deep Network

Data Availability Statement: The EEG dataset is available online at https://mindbigdata.com/opendb/ (Accessed on 12 February 2020).Interpretation of neural activity in response to stimulations received from the surrounding environment is necessary to realize automatic brain decoding. Analyzing the brain recordings corresponding to visual stimulation helps to infer the effects of perception occurring by vision on brain activity. In this paper, the impact of arithmetic concepts on vision-related brain records has been considered and an efficient convolutional neural network-based generative adversarial network (CNN-GAN) is proposed to map the electroencephalogram (EEG) to salient parts of the image stimuli. The first part of the proposed network consists of depth-wise one-dimensional convolution layers to classify the brain signals into 10 different categories according to Modified National Institute of Standards and Technology (MNIST) image digits. The output of the CNN part is fed forward to a fine-tuned GAN in the proposed model. The performance of the proposed CNN part is evaluated via the visually provoked 14-channel MindBigData recorded by David Vivancos, corresponding to images of 10 digits. An average accuracy of 95.4% is obtained for the CNN part for classification. The performance of the proposed CNN-GAN is evaluated based on saliency metrics of SSIM and CC equal to 92.9% and 97.28%, respectively. Furthermore, the EEG-based reconstruction of MNIST digits is accomplished by transferring and tuning the improved CNN-GAN’s trained weights.This research received no external funding

Optimization of convolutional neural networks for image classification using genetic algorithms and bayesian optimization

Notwithstanding the recent successes of deep convolutional neural networks for classification tasks, they are sensitive to the selection of their hyperparameters, which impose an exponentially large search space on modern convolutional models. Traditional hyperparameter selection methods include manual, grid, or random search, but these require expert knowledge or are computationally burdensome. Divergently, Bayesian optimization and evolutionary inspired techniques have surfaced as viable alternatives to the hyperparameter problem. Thus, an alternative hybrid approach that combines the advantages of these techniques is proposed. Specifically, the search space is partitioned into discrete-architectural, and continuous and categorical hyperparameter subspaces, which are respectively traversed by a stochastic genetic search, followed by a genetic-Bayesian search. Simulations on a prominent image classification task reveal that the proposed method results in an overall classification accuracy improvement of 0.87% over unoptimized baselines, and a greater than 97% reduction in computational costs compared to a commonly employed brute force approach.Electrical and Mining EngineeringM. Tech. (Electrical Engineering

El modelo cortical HTM y su aplicación al conocimiento lingüístico

El problema que aborda este trabajo de investigación es encontrar un modelo neurocomputacional de representación y comprensión del conocimiento léxico, utilizando para ello el algoritmo cortical HTM, que modela el mecanismo según el cual se procesa la información en el neocórtex humano. La comprensión automática del lenguaje natural implica que las máquinas tengan un conocimiento profundo del lenguaje natural, lo que, actualmente, está muy lejos de conseguirse. En general, los modelos computacionales para el Procesamiento del Lenguaje Natural (PLN), tanto en su vertiente de análisis y comprensión como en la de generación, utilizan algoritmos fundamentados en modelos matemáticos y lingüísticos que intentan emular la forma en la que tradicionalmente se ha procesado el lenguaje, por ejemplo, obteniendo la estructura jerárquica implícita de las frases o las desinencias de las palabras. Estos modelos son útiles porque sirven para construir aplicaciones concretas como la extracción de datos, la clasificación de textos o el análisis de opinión. Sin embargo, a pesar de su utilidad, las máquinas realmente no entienden lo que hacen con ninguno de estos modelos. Por tanto, la pregunta que se aborda en este trabajo es si, realmente, es posible modelar computacionalmente los procesos neocorticales humanos que regulan el tratamiento de la información de tipo semántico del léxico. Esta cuestión de investigación constituye el primer nivel para comprender el procesamiento del lenguaje natural a niveles lingüísticos superiores..