In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision.

Very Deep Convolutional Networks for Large-Scale Image Recognition

We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.

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Going deeper with convolutions

Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

Deep Learning

Training Deep Neural Networks is complicated by the fact that the distribution of each layer's inputs changes during training, as the parameters of the previous layers change. This slows down the training by requiring lower learning rates and careful parameter initialization, and makes it notoriously hard to train models with saturating nonlinearities. We refer to this phenomenon as internal covariate shift, and address the problem by normalizing layer inputs. Our method draws its strength from making normalization a part of the model architecture and performing the normalization for each training mini-batch. Batch Normalization allows us to use much higher learning rates and be less careful about initialization, and in some cases eliminates the need for Dropout. Applied to a state-of-the-art image classification model, Batch Normalization achieves the same accuracy with 14 times fewer training steps, and beats the original model by a significant margin. Using an ensemble of batch-normalized networks, we improve upon the best published result on ImageNet classification: reaching 4.82% top-5 test error, exceeding the accuracy of human raters.

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Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift

Fetal ultrasound is an important component of antenatal care, but shortage of adequately trained healthcare workers has limited its adoption in low-to-middle-income countries. This study investigated the use of artificial intelligence for fetal ultrasound in under-resourced settings.Blind sweep ultrasounds, consisting of six freehand ultrasound sweeps, were collected by sonographers in the USA and Zambia, and novice operators in Zambia. We developed artificial intelligence (AI) models that used blind sweeps to predict gestational age (GA) and fetal malpresentation. AI GA estimates and standard fetal biometry estimates were compared to a previously established ground truth, and evaluated for difference in absolute error. Fetal malpresentation (non-cephalic vs cephalic) was compared to sonographer assessment. On-device AI model run-times were benchmarked on Android mobile phones.Here we show that GA estimation accuracy of the AI model is non-inferior to standard fetal biometry estimates (error difference -1.4 ± 4.5 days, 95% CI -1.8, -0.9, n = 406). Non-inferiority is maintained when blind sweeps are acquired by novice operators performing only two of six sweep motion types. Fetal malpresentation AUC-ROC is 0.977 (95% CI, 0.949, 1.00, n = 613), sonographers and novices have similar AUC-ROC. Software run-times on mobile phones for both diagnostic models are less than 3 s after completion of a sweep.The gestational age model is non-inferior to the clinical standard and the fetal malpresentation model has high AUC-ROCs across operators and devices. Our AI models are able to run on-device, without internet connectivity, and provide feedback scores to assist in upleveling the capabilities of lightly trained ultrasound operators in low resource settings. 

/pdf/a-mobile-optimized-artificial-intelligence-system-for-1wkudydd.pdf

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Author Correction: Detection of anaemia from retinal fundus images via deep learning.

리커런트 뉴럴 네트워크를 이용하여 충족되는 컨디션들의 가능성을 예측하기 위한 컴퓨터 저장 매체 상에 인코딩된 컴퓨터 프로그램을 포함하는 방법들, 시스템들, 및 장치가 개시된다. 상기 시스템들 중 하나는, 복수의 타임 스텝들 각각에서 각각의 입력을 포함하는 시간적 시퀀스를 프로세싱하도록 구성되고 그리고 하나 이상의 리커런트 뉴럴 네트워크 계층들; 및 하나 이상의 로지스틱 회귀 노드들을 포함하고, 상기 로지스틱 회귀 노드들 각각은 미리결정된 컨디션들의 세트의 각각의 컨디션에 대응하고, 상기 로지스틱 회귀 노드들 각각은 상기 복수의 타임 스텝들 각각에 대하여, 상기 타임 스텝에 대한 네트워크 내부 상태를 수신하고, 그리고 상기 타임 스텝에 대한 상기 대응 컨디션에 대한 미래 컨디션 점수를 생성하도록, 상기 로지스틱 회귀 노드의 파라미터들의 세트의 현재 값들에 따라 상기 타임 스텝에 대한 네트워크 내부 상태를 프로세싱한다.

리커런트 뉴럴 네트워크를 이용한 컨디션들의 충족 가능성에 대한 예측

A method, computer readable storage device, and apparatus for determining the distance a computing device is located from a user's face. An image of an individual is obtained. A first pupil location and a second pupil location are identified based on the obtained image. A first distance between the identified first and second pupil location is determined. A second distance between the individual and the computing device is determined based on the determined first distance between the identified first and second pupil locations.

Determining a viewing distance for a computing device

Recently it was shown that blood hemoglobin concentration could be predicted from retinal fundus photographs by deep learning models. However, it is unclear whether the models were quantifying current blood hemoglobin level, or estimating based on subjects' pretest probability of having anemia. Here, we conducted an observational study with 14 volunteers who donated blood at an on site blood drive held by the local blood center (ie, at which time approximately 10% of their blood was removed). When the deep learning model was applied to retinal fundus photographs taken before and after blood donation, it detected a decrease in blood hemoglobin concentration within each subject at 2-3 days after donation, suggesting that the model was quantifying subacute hemoglobin changes instead of predicting subjects' risk. Additional randomized or controlled studies can further validate this finding.

Greg S. Corrado

Papers

A mobile-optimized artificial intelligence system for gestational age and fetal malpresentation assessment

Author Correction: Detection of anaemia from retinal fundus images via deep learning.

리커런트 뉴럴 네트워크를 이용한 컨디션들의 충족 가능성에 대한 예측

Determining a viewing distance for a computing device

Retinal fundus photographs capture hemoglobin loss after blood donation