#Automated Optical Inspection #Automated Machine Learning

Automated Defect Detection (complete pipeline and demo)

Quality Check (QC) is an integral part of each manufacturing process. Every serious manufacturing team performs multiple quality checks both during and at the end of the production process. For example, medical sensor production can have the following quality checks in place: visual inspection at each step of the production (In Process Control or IPC) visual inspection of the final product In-use tests, i.e using the final product in a real-world environment to test accuracy, stability and detect possible defects Problem with some quality checks (for example, in-use tests) is that they degrade the product quality, hence can’t be used extensively. ...

#Semantic Image Segmentation #Automated machine learning

Image segmentation: change the color of your car! Step-by-step guide.

Have you ever wondered what your car would look like in another color? Or are you just interested in training a deep learning model for image segmentation tasks with no theoretical knowledge required? Or even both? Then this post is for you! There will be two main parts: first, how to create a dataset for an image segmentation task and then how to create and train a model. If you’re only interested in the second part, you can read it directly! ...

#Image classification #Drug discovery #Automated machine learning

Cellular Image Classification for Drug Discovery

Drug development process is divided into 5 steps in the US: Discovery and development Preclinical studies Clinical development FDA Review Post-market monitoring It’s a very lengthy process, which might take 12–15 years and cost around 1 billion dollar. This article explains the basis of drug development very nicely. Pharmaceutical industry desperately needs to reduce the development time and costs. In this article, I am going to write about how recent advances in deep learning help to improve the early drug discovery process - the 1st step of drug development. ...

#Image classification #Medical image analysis #Automated machine learning

Blindness detection with Artificial Intelligence

Imagine being able to detect blindness before it happened. Millions of people suffer from Diabetic retinopathy (DR), the leading cause of blindness among working aged adults. Diabetic retinopathy is an eye disease associated with long-standing diabetes. This happens when high blood sugar levels cause damage to blood vessels in the retina. These blood vessels can swell and leak. Or they can close, stopping blood from passing through. Sometimes abnormal new blood vessels grow on the retina. ...

#Image classification #Medical image analysis #Automated machine learning

Detecting eye disease using Artificial Intelligence

Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population of the developed world. It is estimated to affect over 93 million people. Progression to vision impairment can be slowed or averted if DR is detected in time, however this can be difficult as the disease often shows few symptoms until it is too late to provide effective treatment. What is Diabetic Retinopathy? Diabetic Retinopathy is an eye disease associated with long-standing diabetes. ...

#Medical image analysis #Object detection #Automated machine learning

Pneumonia Detection from chest radiograph (CXR)

Pneumonia accounts for over 15% of all deaths of children under 5 years old internationally. In 2015, 920,000 children under the age of 5 died from the disease. While common, accurately diagnosing pneumonia is difficult. It requires review of a chest radiograph (CXR) by highly trained specialists and confirmation through clinical history, vital signs and laboratory exams. An example chest radiograph looks like this: Pneumonia usually manifests as an area or areas of increased lung opacity on CXR. ...