Goto

Collaborating Authors

Breast cancer classification with Keras and Deep Learning - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to train a Keras deep learning model to predict breast cancer in breast histology images. Back 2012-2013 I was working for the National Institutes of Health (NIH) and the National Cancer Institute (NCI) to develop a suite of image processing and machine learning algorithms to automatically analyze breast histology images for cancer risk factors, a task that took trained pathologists hours to complete. Back then deep learning was not as popular and "mainstream" as it is now. For example, the ImageNet image classification challenge had only launched in 2009 and it wasn't until 2012 that Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton won the competition with the now infamous AlexNet architecture. To analyze the cellular structures in the breast histology images we were instead leveraging basic computer vision and image processing algorithms, but combining them in a novel way.


Convolutional Neural Network for Breast Cancer Classification - KDnuggets

#artificialintelligence

Breast cancer is the second most common cancer in women and men worldwide. In 2012, it represented about 12 percent of all new cancer cases and 25 percent of all cancers in women. Breast cancer starts when cells in the breast begin to grow out of control. These cells usually form a tumor that can often be seen on an x-ray or felt as a lump. The tumor is malignant (cancer) if the cells can grow into (invade) surrounding tissues or spread (metastasize) to distant areas of the body.


Transfer Learning with Keras and Deep Learning - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.


Transfer Learning with Keras and Deep Learning - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to perform transfer learning with Keras, Deep Learning, and Python on your own custom datasets. You're just hired by Yelp to work in their computer vision department. Yelp has just launched a new feature on its website that allows reviewers to take photos of their food/dishes and then associate them with particular items on a restaurant's menu. Certain nefarious users aren't taking photos of their dishes…instead, they are taking photos of… (well, you can probably guess). Figure out how to create an automated computer vision application that can distinguish between "food" and "not food", thereby allowing Yelp to continue with their new feature launch and provide value to their users. So, how are you going to build such an application? The answer lies in transfer learning via deep learning. Today marks the start of a brand new set of tutorials on transfer learning using Keras.


Deep Learning and Medical Image Analysis with Keras - PyImageSearch

#artificialintelligence

In this tutorial, you will learn how to apply deep learning to perform medical image analysis. Specifically, you will discover how to use the Keras deep learning library to automatically analyze medical images for malaria testing. Such a deep learning medical imaging system can help reduce the 400,000 deaths per year caused by malaria. Today's tutorial was inspired by two sources. They've helped me as I've been studying deep learning. I live in an area of Africa that is prone to disease, especially malaria. I'd like to be able to apply computer vision to help reduce malaria outbreaks. Do you have any tutorials on medical imaging?