PerceptiLabs

healthcare

Use Case: Highlighting Blood Cells in Dark-field Microscopy Using Image Segmentation

An application of dark-field microscopy is to view blood sample features such as blood cells. We created a use case for applying image segmentation to dark-field microscopy images using PerceptiLabs.

Machine Learning

A Guide to Using U-Nets for Image Segmentation

Image segmentation is a powerful technique to locate objects or boundaries in images on a per-pixel level. In this blog we take a quick look at how U-Nets work, explore how they build on standard CNNs, and show how easy it is to build and iterate on U-Nets in PerceptiLabs.

Personal Assitant

Use Case: A Voice Recognition model using Image Recognition

Spoken language is the most natural form of human interaction, and thanks to advancements in modern-day ML, it's fast becoming the perfect way to interact with computers.

Retail

Use Case: Classifying Fruit

The handling of perishable foods like fruits and vegetables in a supply chain can involve many processes. Traditionally, these processes were done by hand, but we built an image recognition model in PerceptiLabs, to see if it could identify different types of fruits simply by analyzing images.

Machine Learning

Working With Unbalanced Datasets

ML practitioners wrangle data to create the best training datasets possible for their models. This generally means that the data is unbiased, well-structured, and will help to train a model that gives good predictive performance in the real world. Whether you collect data yourself from the real world, use an

healthcare

Use Case: Retinal OCT

The human eye is a complex sensing organ that reacts to light to provide us with a field of view into the world around us. Taking care of our eyes is an important aspect of our overall healthcare. However, our eyes are susceptible to a number of diseases, some of

Manufacturing

Use Case: Fabric Stain Classification

The global textile industry impacts nearly every human being on the planet and had an estimated size of $1000.3 billion USD in 20201. It includes the production, refinement, and sale of both synthetic and natural fibers used in thousands of industries. The high demand for quality textiles has led

IIoT

Use Case: Classifying Wood Veneers Into Dry and Wet

Industrial IoT is playing a significant role in Industry 4.0, especially for automating manufacturing processes, including manufacturing wood veneers. We built an image recognition model that could identify veneers as either dry or wet.

healthcare

Use Case: Detecting Defective Pills

To help automate the detection of defects in medical pills we set out to build an image recognition model in PerceptiLabs that could identify defective pills by analyzing images.

Health and Safety

Use Case: Classifying Ways to Wear a Face Mask

The spread of COVID-19 required that masks be worn correctly. We set out to build an image recognition model in PerceptiLabs which could classify different ways that people wear masks.

Optometry

Use Case: Ocular Disease Recognition

We decided to build an ML model in PerceptiLabs that applies image recognition techniques on fundus images to detect possible cataracts in patients. Using a model like this could help doctors, optometrists, and researchers to more easily classify and detect such conditions.

medical

Use Case: Breast Cancer Detection

Inspired by the growing use of ML in healthcare, we decided to build an image recognition model in PerceptiLabs trained on malignant and benign microscopic biopsy images.

Top Five TensorFlow Issues Solved by PerceptiLabs

TensorFlow is a powerful, open-ended API for building ML models, but it does have shortcomings. In our blog An Overview of TensorFlow and how PerceptiLabs Makes it Easier, we discussed how TensorFlow works and how PerceptiLabs' GUI and visual API make it easier to build TensorFlow models.

Manufacturing

Use Case: Air Conditioner Piston Check

With the growing use of computer vision in Industrial IoT (IIoT) and Industry 4.0 to analyze and detect defects, we were inspired to build an image recognition model that can classify images of AC pistons.

Machine Learning

Case Study: Genetic Screening Using CNNs in PerceptiLabs

One group of scientists leveraging ML is the Buchser lab at Washington University in Saint Louis School of Medicine in the Department of Genetics. There, Dr. Buchser and his team are working in functional genomics, High-Throughput Screening, and Next-Generation Sequencing.

Manufacturing

Use Case: Defect Detection in Metal Surfaces

With the rise of Industrial IoT (IIoT) and Industry 4.0, ML is playing an ever-increasing role in enabling automation. With this in mind, we built an ML model that can classify defects on metal materials using image recognition.

Machine Learning

Use Case: Skin Cancer Classification

To accurately diagnose cancer cases, doctors need every possible tool at their disposal. In the case of skin cancer, the affected areas can often be visually examined without the need for invasive diagnostics typically involved with cancers inside the body.

Use Case

Use Case: Classifying Chest X-Rays to Detect Pneumonia

To accurately diagnose ailments, doctors need every possible tool at their disposal. This Use Case shows how to classify chest x-rays to detect Pneumonia.

image classification

Use Case: Automated Weather Analysis Using Image Recognition

Many industries have the need to identify current and past weather conditions. The data helps them plan, organize, and/or optimize their operations. This Use Case shows automated weather analysis using Image Recognition.

Machine Learning

Essential Lingo for the Aspiring ML Practitioner

Every industry has its lingo and machine learning (ML) is no different. Here's a brief checklist of key terminology that all aspiring ML developers should be familiar with when building models.

Machine Learning

Tips to Steer Your Developer Career Towards Machine Learning

Excited by the hype around machine learning (ML)? Perhaps you’d like to transition into the field but you’re not sure where to start. Learn more here.

Machine Learning

Inference for the Real World: What to do with your ML Model Once You've Trained it

The end result of the ML modeling process is a file containing the model's trained weights ready for inference. But once you have this file, how do you actually use it for inference?

Machine Learning

The Power of Abstraction: How ML Frameworks and PerceptiLabs Provide a more Human-Readable Language for Describing ML Models

Everyone wants to enhance their understanding of machine learning or ML these days. But what does it really mean to do ML?

Loss Function

Choosing and Customizing Loss Functions for Image Processing

A loss function is the algorithm used during training to determine how well your model can make predictions using a given set of weights and biases. In this blog we review how a loss function works, its role and importance in optimization, how to choose one, and how to work with it in Perceptilabs.

visual modeling tool

Comparing PerceptiLabs to Pure Code in Transfer Learning

Pure-code ML frameworks like TensorFlow are powerful tools, but are often limited when it comes to the insight and explainability of models. In this blog we explore these issues, and introduce PerceptiLabs to solve these challenges.