# Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning

Abstract: Traditionally, medical discoveries are made by observing associations, making hypotheses from them and then designing and running experiments to test the hypotheses. However, with medical images, observing and quantifying associations can often be difficult because of the wide variety of features, patterns, colours, values and shapes that are present in real data. Here, we show that deep learning can extract new knowledge from retinal fundus images. Using deep-learning models trained on data from 284,335 patients and validated on two independent datasets of 12,026 and 999 patients, we predicted cardiovascular risk factors not previously thought to be present or quantifiable in retinal images, such as age (mean absolute error within 3.26 years), gender (area under the receiver operating characteristic curve (AUC) = 0.97), smoking status (AUC = 0.71), systolic blood pressure (mean absolute error within 11.23 mmHg) and major adverse cardiac events (AUC = 0.70). We also show that the trained deep-learning models used anatomical features, such as the optic disc or blood vessels, to generate each prediction.

Anúncios

# Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

ABSTRACT— Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.

CONCLUSION AND FUTURE WORK I proposed a machine learning model for automatic DBMS diagnosis. The proposed model detects anomaly periods from reconstruct error with deep autoencoder. I also verified empirically that temporal normalization is essential when input data is non-stationary multivariate time series. With SPC approach, time period is considered anomaly period when reconstruction error is outside of control limit. According types or users of DBMSs, decision rules that are used in SPC can be added. For example, warning line with 2 sigma can be utilized to decide whether it is anomaly or not [12, 13]. In this paper, anomaly detection test is proceeded in other DBMSs whose data is not used in training, because performance of basic pre-trained model is important in service providers’ perspective. Efficacy of detection performance is validated with blind test and DBAs’ opinions. The result of automatic anomaly diagnosis would help DB consultants save time for anomaly periods and main wait events. Thus, they can concentrate on only making solution when DB disorders occur. For better performance of anomaly detection, additional training can be proceeded after pre-trained model is adopted. In addition, recurrent and convolutional neural network can be used in reconstruction part to capture hidden representation of sequential and local relationship. If anomaly labeled data is generated, detection result can be analyzed with numerical performance measures. However, in practice, it is hard to secure labeled anomaly dataset according to each DBMS. Proposed model is meaningful in unsupervised anomaly detection model that doesn’t need labeled data and can be generalized to other DBMSs with pre-trained model

Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis

# Cardiologist-Level Arrhythmia Detection with Convolutional Neural Networks

Conclusion We develop a model which exceeds the cardiologist performance in detecting a wide range of heart arrhythmias from single-lead ECG records. Key to the performance of the model is a large annotated dataset and a very deep convolutional network which can map a sequence of ECG samples to a sequence of arrhythmia annotations. On the clinical side, future work should investigate extending the set of arrhythmias and other forms of heart disease which can be automatically detected with high-accuracy from single or multiple lead ECG records. For example we do not detect Ventricular Flutter or Fibrillation. We also do not detect Left or Right Ventricular Hypertrophy, Myocardial Infarction or a number of other heart diseases which do not necessarily exhibit as arrhythmias. Some of these may be difficult or even impossible to detect on a single-lead ECG but can often be seen on a multiple-lead ECG. Given that more than 300 million ECGs are recorded annually, high-accuracy diagnosis from ECG can save expert clinicians and cardiologists considerable time and decrease the number of misdiagnoses. Furthermore, we hope that this technology coupled with low-cost ECG devices enables more widespread use of the ECG as a diagnostic tool in places where access to a cardiologist is difficult.

# L2 Regularization versus Batch and Weight Normalization

Discussion: Normalization, either Batch Normalization, Layer Normalization, or Weight Normalization makes the learned function invariant to scaling of the weights w. This scaling is strongly affected by regularization. We know of no first order gradient method that can fully eliminate this effect. However, a direct solution of forcing kwk = 1 solves the problem. By doing this we also remove one hyperparameter from the training procedure. As noted by Salimans & Kingma (2016), the effect of weight and batch normalization on the effective learning rate might not necessarily be bad. If no regularization is used, then the norm of the weights tends to increase over time, and so the effective learning rate decreases. Often that is a desirable thing, and many training methods lower the learning rate explicitly. However, the decrease of effective learning rate can be hard to control, and can depend a lot on initial steps of training, which makes it harder to reproduce results. With batch normalization we have added two additional parameters, γ and β, and it of course makes sense to also regularize these. In our experiments we did not use regularization for these parameters, though preliminary experiments show that regularization here does not affect the results. This is not very surprising, since with rectified linear activation functions, scaling of γ also has no effect on the function value in subsequent layers. So the only parameters that are actually regularized are the γ’s for the last layer of the network.

# SLIM: Sparse Linear Methods for Top-N Recommender Systems

Um ótimo artigo de base teórica, relativo a geração de Top-N recomendações em cenários bem esparsos (e.g. sistema de rating 0-5 em que poucas pessoas fazem a anotação do rating, etc).

Recentemente, esse problema de recomendar dentro de uma matriz muito esparsa foi o motivo pelo qual o Netflix mudou o seu sistema de Rating que era de 1 a 5 para jóia ou ruim.

Em todo o caso vale a pena a leitura para ver a forma na qual os autores estão trabalhando nesse tipo de desafio.

Abstract: This paper focuses on developing effective and efficient algorithms for top-N recommender systems. A novel Sparse Linear Method (SLIM) is proposed, which generates top-N recommendations by aggregating from user purchase/rating profiles. A sparse aggregation coefficient matrix W is learned from SLIM by solving an 1-norm and 2-norm regularized optimization problem. W is demonstrated to produce high quality recommendations and its sparsity allows SLIM to generate recommendations very fast. A comprehensive set of experiments is conducted by comparing the SLIM method and other state-of-the-art top-N recommendation methods. The experiments show that SLIM achieves significant improvements both in run time performance and recommendation quality over the best existing methods.

# Melhores papers de Deep Learning de 2012 até 2016

Para estudar com lápis na mão, e café na caneca.

Via Kdnuggets

1. Understanding / Generalization / Transfer

Distilling the knowledge in a neural network (2015), G. Hinton et al. [pdf]

2. Optimization / Training Techniques

Batch normalization: Accelerating deep network training by reducing internal covariate shift (2015), S. Loffe and C. Szegedy [pdf]

3. Unsupervised / Generative Models

Unsupervised representation learning with deep convolutional generative adversarial networks (2015), A. Radford et al. [pdf]

4. Convolutional Neural Network Models

Deep residual learning for image recognition (2016), K. He et al. [pdf]

5. Image: Segmentation / Object Detection

Fast R-CNN (2015), R. Girshick [pdf]

6. Image / Video / Etc.

Show and tell: A neural image caption generator (2015), O. Vinyals et al. [pdf]

7. Natural Language Processing / RNNs

Learning phrase representations using RNN encoder-decoder for statistical machine translation (2014), K. Cho et al. [pdf]

8. Speech / Other Domain

Speech recognition with deep recurrent neural networks (2013), A. Graves [pdf]

9. Reinforcement Learning / Robotics

Human-level control through deep reinforcement learning (2015), V. Mnih et al. [pdf]

10. More Papers from 2016

Domain-adversarial training of neural networks (2016), Y. Ganin et al. [pdf]

# Porque o xGBoost ganha todas as competições de Machine Learning

Uma (longa e) boa resposta está nesta tese de Didrik Nielsen.

16128_FULLTEXT

Abstract: Tree boosting has empirically proven to be a highly effective approach to predictive modeling.
It has shown remarkable results for a vast array of problems.
For many years, MART has been the tree boosting method of choice.
More recently, a tree boosting method known as XGBoost has gained popularity by winning numerous machine learning competitions.
In this thesis, we will investigate how XGBoost differs from the more traditional MART.
We will show that XGBoost employs a boosting algorithm which we will term Newton boosting. This boosting algorithm will further be compared with the gradient boosting algorithm that MART employs.
Moreover, we will discuss the regularization techniques that these methods offer and the effect these have on the models.
In addition to this, we will attempt to answer the question of why XGBoost seems to win so many competitions.
To do this, we will provide some arguments for why tree boosting, and in particular XGBoost, seems to be such a highly effective and versatile approach to predictive modeling.
The core argument is that tree boosting can be seen to adaptively determine the local neighbourhoods of the model. Tree boosting can thus be seen to take the bias-variance tradeoff into consideration during model fitting. XGBoost further introduces some subtle improvements which allows it to deal with the bias-variance tradeoff even more carefully.

Conclusion: After determining the different boosting algorithms and regularization techniques these methods utilize and exploring the effects of these, we turned to providing arguments for why XGBoost seems to win “every” competition. To provide possible answers to this question, we first gave reasons for why tree boosting in general can be an effective approach. We provided two main arguments for this. First off, additive tree models can be seen to have rich representational abilities. Provided that enough trees of sufficient depth is combined, they are capable of closely approximating complex functional relationships, including high-order interactions. The most important argument provided for the versatility of tree boosting however, was that tree boosting methods are adaptive. Determining neighbourhoods adaptively allows tree boosting methods to use varying degrees of flexibility in different parts of the input space. They will consequently also automatically perform feature selection. This also makes tree boosting methods robust to the curse of dimensionality. Tree boosting can thus be seen actively take the bias-variance tradeoff into account when fitting models. They start out with a low variance, high bias model and gradually reduce bias by decreasing the size of neighbourhoods where it seems most necessary. Both MART and XGBoost have these properties in common. However, compared to MART, XGBoost uses a higher-order approximation at each iteration, and can thus be expected to learn “better” tree structures. Moreover, it provides clever penalization of individual trees. As discussed earlier, this can be seen to make the method even more adaptive. It will allow the method to adaptively determine the appropriate number of terminal nodes, which might vary among trees. It will further alter the learnt tree structures and leaf weights in order to reduce variance in estimation of the individual trees. Ultimately, this makes XGBoost a highly adaptive method which carefully takes the bias-variance tradeoff into account in nearly every aspect of the learning process.

# Novel Revenue Development and Forecasting Model using Machine Learning Approaches for Cosmetics Enterprises.

Abstract:In the contemporary information society, constructing an effective sales prediction model is challenging due to the sizeable amount of purchasing information obtained from diverse consumer preferences. Many empirical cases shown in the existing literature argue that the traditional forecasting methods, such as the index of smoothness, moving average, and time series, have lost their dominance of prediction accuracy when they are compared with modern forecasting approaches such as neural network (NN) and support vector machine (SVM) models. To verify these findings, this paper utilizes the Taiwanese cosmetic sales data to examine three forecasting models: i) the back propagation neural network (BPNN), ii) least-square support vector machine (LSSVM), and iii) auto regressive model (AR). The result concludes that the LS-SVM has the smallest mean absolute percent error (MAPE) and largest Pearson correlation coefficient ( R2 ) between model and predicted values.

# Falhas na abordagem de Deep Learning: Arquiteturas e Meta-parametrização

O maior desafio corrente enfrentado pela indústria no que diz respeito à Deep Learning está sem sombra de dúvidas na parte computacional em que todo o mercado está absorvendo tanto os serviços de nuvem para realizar cálculos cada vez mais complexos como também bem como investindo em capacidade de computação das GPU.

Entretanto, mesmo com o hardware nos dias de hoje já ser um commodity, a academia está resolvendo um problema que pode revolucionar a forma na qual se faz Deep Learning que é no aspecto arquitetural/parametrização.

Esse comentário da thread diz muito a respeito desse problema em que o usuário diz:

The main problem I see with Deep Learning: too many parameters.

When you have to find the best value for the parameters, that’s a gradient search by itself. The curse of meta-dimensionality.

Ou seja, mesmo com toda a disponibilidade do hardware a questão de saber qual é o melhor arranjo arquitetural de uma rede neural profunda? ainda não está resolvido.

Este paper do Shai Shalev-Shwartz , Ohad Shamir, e Shaked Shammah chamado “Failures of Deep Learning” expõe esse problema de forma bastante rica inclusive com experimentos (este é o repositório no Git).

Os autores colocam que os pontos de falha das redes Deep Learning que são a) falta de métodos baseados em gradiente para otimização de parâmetros, b) problemas estruturais nos algoritmos de Deep Learning na decomposição dos problemas, c) arquitetura e d) saturação das funções de ativação.

Em outras palavras, o que pode estar acontecendo em grande parte das aplicações de Deep Learning é que o tempo de convergência poderia ser muito menor ainda, se estes aspectos já estivessem resolvidos.

Com isso resolvido, grande parte do que conhecemos hoje como indústria de hardware para as redes Deep Learning seria ou sub-utilizada ao extremo (i.e. dado que haverá uma melhora do ponto de vista de otimização arquitetural/algorítmica) ou poderia ser aproveitada para tarefas mais complexas (e.g. como reconhecimento de imagens com baixo número de pixels).

Desta forma mesmo adotando uma metodologia baseada em hardware como a indústria vem fazendo, há ainda muito espaço de otimização em relação às redes Deep Learning do ponto de vista arquitetural e algorítmico.

Abaixo uma lista de referências direto do Stack Exchange para quem quiser se aprofundar mais no assunto:

Algoritmos Neuro-Evolutivos

Miscelânea:

PS: O WordPress retirou a opção de justificar texto, logo desculpem de antemão a aparência amadora do blog nos próximos dias.

# Akid: Uma biblioteca de Redes Neurais para pesquisa e produção

Finalmente começaram a pensar em eliminar esse vale entre ciência/academia e indústria.

Akid: A Library for Neural Network Research and Production from a Dataism Approach – Shuai Li
Abstract: Neural networks are a revolutionary but immature technique that is fast evolving and heavily relies on data. To benefit from the newest development and newly available data, we want the gap between research and production as small as possibly. On the other hand, differing from traditional machine learning models, neural network is not just yet another statistic model, but a model for the natural processing engine — the brain. In this work, we describe a neural network library named {\texttt akid}. It provides higher level of abstraction for entities (abstracted as blocks) in nature upon the abstraction done on signals (abstracted as tensors) by Tensorflow, characterizing the dataism observation that all entities in nature processes input and emit out in some ways. It includes a full stack of software that provides abstraction to let researchers focus on research instead of implementation, while at the same time the developed program can also be put into production seamlessly in a distributed environment, and be production ready. At the top application stack, it provides out-of-box tools for neural network applications. Lower down, akid provides a programming paradigm that lets user easily build customized models. The distributed computing stack handles the concurrency and communication, thus letting models be trained or deployed to a single GPU, multiple GPUs, or a distributed environment without affecting how a model is specified in the programming paradigm stack. Lastly, the distributed deployment stack handles how the distributed computing is deployed, thus decoupling the research prototype environment with the actual production environment, and is able to dynamically allocate computing resources, so development (Devs) and operations (Ops) could be separated.