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@DIAGNijmegen

Diagnostic Image Analysis Group

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  1. neural-odes-segmentation neural-odes-segmentation Public

    Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

    Python 109 28

  2. StreamingCNN StreamingCNN Public

    To 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 …

    Jupyter Notebook 100 24

  3. pathology-whole-slide-data pathology-whole-slide-data Public

    A package for working with whole-slide data including a fast batch iterator that can be used to train deep learning models.

    Python 98 30

  4. pathology-streaming-pipeline pathology-streaming-pipeline Public

    Use streaming to train whole-slides images with single image-level labels, by reducing GPU memory requirements with 99%.

    Python 78 10

  5. picai_labels picai_labels Public

    Annotations for the PI-CAI Challenge: Public Training and Development Dataset

    58 25

  6. pathology-hooknet pathology-hooknet Public

    Jupyter Notebook 57 11

Repositories

Showing 10 of 196 repositories
  • rse-grand-challenge-forge Public

    Generation of challenge packs

    DIAGNijmegen/rse-grand-challenge-forge’s past year of commit activity
    Python 0 Apache-2.0 0 5 1 Updated Apr 8, 2025
  • website-content Public

    This repository stores all the content for the diag websites.

    DIAGNijmegen/website-content’s past year of commit activity
    TeX 6 MIT 75 27 (1 issue needs help) 3 Updated Apr 8, 2025
  • rse-pyswot Public

    Python implementation of JetBrains/swot

    DIAGNijmegen/rse-pyswot’s past year of commit activity
    Python 1 Apache-2.0 1 0 0 Updated Apr 8, 2025
  • dragon_prep Public

    Preprocessing scripts for the DRAGON benchmark

    DIAGNijmegen/dragon_prep’s past year of commit activity
    Python 0 Apache-2.0 0 0 0 Updated Apr 4, 2025
  • 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
    Python 0 1 0 0 Updated Apr 4, 2025
  • DIAGNijmegen/dragon_submission_llm_extractinator_qwen2.5-14b’s past year of commit activity
    Python 0 Apache-2.0 0 0 0 Updated Apr 4, 2025
  • DIAGNijmegen/dragon_submission_llm_extractinator_gemma2-2b’s past year of commit activity
    Python 0 Apache-2.0 0 0 0 Updated Apr 4, 2025
  • DIAGNijmegen/dragon_submission_llm_extractinator_llama3.2-3b’s past year of commit activity
    Python 0 Apache-2.0 0 0 0 Updated Apr 4, 2025
  • DIAGNijmegen/dragon_submission_llm_extractinator_gemma2’s past year of commit activity
    Python 0 Apache-2.0 0 0 0 Updated Apr 4, 2025
  • Literature Public

    The literature generated and cited by the group

    DIAGNijmegen/Literature’s past year of commit activity
    TeX 0 3 0 0 Updated Apr 3, 2025

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