Diagnostic Image Analysis Group
- 155 followers
- Radboud University Medical Center, Nijmegen, The Netherlands
- http://www.diagnijmegen.nl
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neural-odes-segmentation
neural-odes-segmentation PublicNeural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
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StreamingCNN
StreamingCNN PublicTo train deep convolutional neural networks, the input data and the activations need to be kept in memory. Given the limited memory available in current GPUs, this limits the maximum dimensions of …
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pathology-whole-slide-data
pathology-whole-slide-data PublicA package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.
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pathology-streaming-pipeline
pathology-streaming-pipeline PublicUse streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.
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picai_labels
picai_labels PublicAnnotations for the PI-CAI Challenge: Public Training and Development Dataset
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Repositories
- unicorn_eval Public
DIAGNijmegen/unicorn_eval’s past year of commit activity - unicorn_baseline Public
This repository provides example code for a Docker container for the UNICORN Challenge, enabling foundation model submissions with automated data handling, processing, and necessary dependencies.
DIAGNijmegen/unicorn_baseline’s past year of commit activity - oncology-ULS-fast-for-challenge Public Forked from RianneAr/ULS_fast_for_challenge
A version of the baseline ULS model that works with smaller input data and is more shallow. This version crops the inputs and then pads them after inference, so that it can run with the orignal ULS challenge.
DIAGNijmegen/oncology-ULS-fast-for-challenge’s past year of commit activity - unicorn_baseline_template Public
DIAGNijmegen/unicorn_baseline_template’s past year of commit activity - dragon_baseline Public
Baseline training algorithm for the DRAGON challenge (dragon.grand-challenge.org)
DIAGNijmegen/dragon_baseline’s past year of commit activity - llm_extractinator Public
This project enables the efficient extraction of structured data from unstructured text using large language models (LLMs). It provides a flexible configuration system and supports a variety of tasks.
DIAGNijmegen/llm_extractinator’s past year of commit activity
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