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Building a medical image search platform on AWS

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Improving radiologist efficiency and preventing burnout is a primary goal for healthcare providers. A nationwide study published in Mayo Clinic Proceedings in 2015 showed radiologist burnout percentage at a concerning 61% [1]. In additon, the report concludes that "burnout and satisfaction with work-life balance in US physicians worsened from 2011 to 2014. More than half of US physicians are now experiencing professional burnout."[2] As technologists, we're looking for ways to put new and innovative solutions in the hands of physicians to make them more efficient, reduce burnout, and improve care quality.


Simplify data annotation and model training tasks with Amazon Rekognition Custom Labels

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For a supervised machine learning (ML) problem, labels are values expected to be learned and predicted by a model. To obtain accurate labels, ML practitioners can either record them in real time or conduct offline data annotation, which are activities that assign labels to the dataset based on human intelligence. However, manual dataset annotation can be tedious and tiring for a human, especially on a large dataset. Even with labels that are obvious to a human to annotate, the process can still be error-prone due to fatigue. As a result, building training datasets takes up to 80% of a data scientist's time.


Using artificial intelligence to detect product defects with AWS Step Functions Amazon Web Services

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Factories that produce a high volume of inventory must ensure that defective products are not shipped. This is often accomplished with human workers on the assembly line or through computer vision. You can build an application that uses a custom image classification model to detect and report back any defects in a product, then takes appropriate action. This method provides a powerful, scalable, and simple solution for quality control. It uses Amazon S3, Amazon SQS, AWS Lambda, AWS Step Functions, and Amazon SageMaker.


Detecting playful animal behavior in videos using Amazon Rekognition Custom Labels

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Historically, humans have observed animal behaviors and applied them for different purposes. For example, behavioral observation is important in animal ecology, such as how often the behaviors are, when the behaviors occur, or whether there is individual difference or not. However, identifying and monitoring these behaviors and movements can be hard and can take a long time. To provide an automation for this workflow, a team from the agile members of pharmaceutical customer (Sumitomo Dainippon Pharma Co., Ltd.) and AWS Solutions Architects created a solution with Amazon Rekognition Custom Labels. Amazon Rekognition Custom Labels makes it easy to label specific movements in images, and train and build a model that detects these movements.


Build Natural Flower Classifier using Amazon Rekognition Custom Labels

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Building your own computer vision model from scratch can be fun and fulfilling. You get to decide your preferred choice of machine learning framework and platform for training and deployment, design your data pipeline and neural network architecture, write custom training and inference scripts, and fine-tune your model algorithm's hyperparameters to get the optimal model performance. On the other hand, this can also be a daunting task for someone who has no or little computer vision and machine learning expertise. This post shows a step-by-step guide on how to build a natural flower classifier using Amazon Rekognition Custom Labels with AWS best practices. Amazon Rekognition Custom Labels is a feature of Amazon Rekognition, one of the AWS AI services for automated image and video analysis with machine learning. It provides Automated Machine Learning (AutoML) capability for custom computer vision end-to-end machine learning workflows.