nnDetection Simultaneous localisation and categorization of objects in medical images, also referred to as medical object detection, is of high clinical relevance because diagnostic decisions depend on rating of objects rather than e.g. pixels. For this task, the cumbersome and iterative process of method configuration constitutes a major research bottleneck. Recently, nnU-Net has tackled this challenge for the task of image segmentation with great success. Following nnU-Net’s agenda, in this work we systematize and automate the configuration process for medical […]

AnimeGAN PyTorch implementation of DCGAN introduced in the paper: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Alec Radford, Luke Metz, Soumith Chintala. Generative Adversarial Networks (GANs) are one of the most popular (and coolest)Machine Learning algorithms developed in recent times. They belong to a set of algorithms called generative models, whichare widely used for unupervised learning tasks which aim to learn the uderlying structure of the given data. As the namesuggests GANs allow you to generate new unseen […]