Being able to automate the detection of metastasised cancer in pathological scans with machine learning and deep neural networks is an area of medical imaging and diagnostics with promising potential for clinical usefulness.
Here we explore a particular dataset prepared for this type of analysis and diagnostics. The PCam dataset is a binary classification image dataset containing approximately 300,000 labeled low-resolution images of lymph node sections extracted from digital histopathological scans. Each image is labelled by trained pathologists for the presence of metastasised cancer.
Using a convolutional neural network, transfer learning, and other hyperparameter optimisations, we show how we can predict the occurrence of cancer in this dataset with an accuracy of 98.6%.
Diabetic Retinopathy is a diabetic realted disease that affects the retina of the eye. Millions around the world suffer from this disease.
Currently, diagnosis happens through the use of a technique called fundus photography, which involves photographing the rear of the eye. Medical screening for diabetic retinopathy occurs around the world, but is more difficult for people living in rural areas.
Using machine learning and computer vision, we attempt to automate the process of diagnosis, which currently is manually being performed doctors. Using an ensemble of B3 and B5 Efficientnets, we achieve a Quadratic Weighted Kappa score of 0.905775. In comparison, the winning solution on Kaggle achieved 0.93612.
Verifai deploys smart technology that secures and protects organisations and their assets. Our platform surfaces data to provide visibility to global shipping and maritime supply chains, solving for problems in industries and areas such as food waste and agriculture, pharmaceuticals, border security and illicit trade.
Planetly is a global, location aware, real-time news app. Powered by a natural language processing API, Planetly automatically parsed location information from news articles across the web and visually aggregated that data onto an easy to explore interface.
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