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Bring a Chair on Rounds and Other Tips — In my last year of medical school, I completed most of my clinical clerkships while pregnant, including an internal medicine sub-internship during my third trimester. …

I am the CEO of an early-stage health tech startup, Cydoc. Over the past four years I’ve interviewed hundreds of people for software engineering positions. …

I am thrilled to announce that as of today, 3,630 whole CT scans from the RAD-ChestCT dataset are publicly available on Zenodo, along with abnormality and location labels! You can access the dataset here. After a multi-year saga of bureaucracy and technical barriers, the data is finally up and you…

SARLE is a customizable technique for extracting structured abnormality and location labels automatically from free text radiology reports — Classification labels specifying the presence and absence of abnormalities are necessary to train computer vision models on radiology images. However, obtaining these classification labels by hand is time-consuming and limits the size of the final dataset as well as the number of abnormalities that can be considered. …

In this post, we’ll compare three related but distinct computer vision tasks that can be tackled with convolutional neural networks: image classification model explanation, weakly supervised segmentation, and fully supervised segmentation. We will consider the similarities and differences in goal, training data, and methods. Note that I’ll be focused on…

Tutorial with Code — In this tutorial, you will learn how to use PyTorch’s inbuilt image data sets, and you will learn how to build your own custom image data sets using any images you want. …

DICOM to NumPy with Python — If you are interested in creating your own medical image data set for a machine learning project, this post is for you. You may also be interested in this post if you work with publicly-available medical imaging datasets and would like some further insight into how these datasets are created. …

Tutorial with Code — This tutorial is based on my repository pytorch-computer-vision which contains PyTorch code for training and evaluating custom neural networks on custom data. By the end of this tutorial, you should be able to: Design custom 2D and 3D convolutional neural networks in PyTorch; Understand image dimensions, filter dimensions, and input…

Tutorial with Code — At the end of this tutorial you should be able to: Load randomly initialized or pre-trained CNNs with PyTorch torchvision.models (ResNet, VGG, etc.) Select out only part of a pre-trained CNN, e.g. only the convolutional feature extractor Automatically calculate the number of parameters and memory requirements of a model with…