Discover DIAGNijmegen/m-heads Open Source project

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MIT License
Created over 4 years ago
Updated about 4 years ago

DIAGNijmegen

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neural-odes-segmentation

Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands

Python

105

StreamingCNN

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 the input data. Here we demonstrate a method to train convolutional neural networks while holding only parts of the image in memory.

Jupyter Notebook

pathology-whole-slide-data

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

Python

pathology-streaming-pipeline

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

Python

pathology-he-auto-augment

H&E tailored Randaugment: automatic data augmentation policy selection for H&E-stained histopathology.

Python

pathology-hooknet

Jupyter Notebook

picai_labels

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

picai_baseline

Baseline AI models for 3D csPCa detection/diagnosis in bpMRI

Python

prostateMR_3D-CAD-csPCa

Hierarchical probabilistic 3D U-Net, with attention mechanisms (—𝘈𝘵𝘵𝘦𝘯𝘵𝘪𝘰𝘯 𝘜-𝘕𝘦𝘵, 𝘚𝘌𝘙𝘦𝘴𝘕𝘦𝘵) and a nested decoder structure with deep supervision (—𝘜𝘕𝘦𝘵++). Built in TensorFlow 2.5. Configured for voxel-level clinically significant prostate cancer detection in multi-channel 3D bpMRI scans.

Python

HoVer-UNet

Python

pathology-artifact-detection

Quality control of whole-slide images through multi-class semantic segmentation of artifacts

Python

ULS23

Repository for the Universal Lesion Segmentation Challenge '23

Python

picai_prep

Preprocessing 3D medical images and image archives —geared towards prostate cancer detection in MRI.

Python

picai_eval

Evaluation of 3D detection and diagnosis performance —geared towards prostate cancer detection in MRI.

Python

Report-Guided-Annotation

Report Guided Annotation

Python

spgnn

Structure and position aware graph neural network for airway labeling

Python

opencxr

A collection of open-source algorithms for chest X-ray analysis

Python

rse-panimg

Conversion of medical images to MHA and TIFF.

Python

AbdomenMRUS-prostate-segmentation

Grand Challenge wrapper for whole-gland prostate segmentation with nnUNet

Python

SPIDER-Baseline-IIS

Python

panda-challenge

Jupyter Notebook

pathology-hooknet-tls

Jupyter Notebook

pathology-tiger-algorithm-example

Example algorithm and docker for the TIGER challenge

Python

pathology-tiger-baseline

Python

panorama_labels

drive-vessels-unet

Python

pathology-whole-slide-packer

extracts tissue sections from one or multiple whole slide images and combines them into a single new slide removing excess white space

Python

website-content

This repository stores all the content for the diag websites.

TeX

covid19-imaging-ai

An organized collection of data, software, initiatives and papers for COVID-19 imaging AI

pathology-he-autoaugmetation

Comparative analysis of 4 state-of-the-art automatic augmentation algorithms in H&E stained histopathology.

Python

MedicalTransferLearning3D-UNet

This repository contains the model used for the paper ''Transfer learning from a sparsely annotated dataset of 3D medical images'' by G. Humpire, C. Jacobs, M. Prokop, B. van Ginneken, N. Lessmann.

Jupyter Notebook

prostateMR-USSL

Uncertainty Aware Self-Supervised Prostate Zonal Segmentation in 3D MRI scans using Probabilistic Attention U-Net..

Python

picai_nnunet_gc_algorithm

nnUNet model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

picai_nndetection_semi_supervised_gc_algorithm

Semi-supervised nnDetection model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

AbdomenMRUS-csPCa-CAD-nnUNet

Clinically Significant Prostate Cancer Detection in bpMRI using models trained with Report Guided Annotations

Python

pathology-streamingclam

Lightstream implementation of the StreamingCLAM model

Python

PANORAMA_baseline

Python

picai_unet_semi_supervised_gc_algorithm

Semi-supervised UNet model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

bodyct-dsb2017-grt123

Repository which contains the code of the grt123 solution from the Kaggle DSB 2017 challenge on lung cancer detection

Python

picai_unet_gc_algorithm

U-Net model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

picai_nnunet_semi_supervised_gc_algorithm

Semi-supervised nnUNet model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

adhesion_detection

Adhesion detection on cine MRI

Jupyter Notebook

rse-gcapi

Python client for the grand-challenge.org API

Python

flare22-brananas

Codebase of team brananas for the FLARE22 challenge

Python

picai_nndetection_gc_algorithm

nnDetection model for 3D csPCa detection/diagnosis in bpMRI, deployable on grand-challenge.org

Python

ACOUSLIC-AI-baseline

Python

ACOUSLIC-AI-evaluation-method

Python

SPIDER-Evaluation

Evaluation code used for the SPIDER Challenge.

Python

bodyct-luna22-ismi-training-baseline

Baseline repo for LUNA22-ismi challenge

Python

stoic2021-finalphase-submission-hal9000

Python

stoic2021-finalphase-submission-code1055

Jupyter Notebook

bodyct-dram

Python

node21-noduledetection

Template for nodule detection algorithm for node21 challenge

Jupyter Notebook

frodo

library for FRODO

Python

node21

rse-pyswot

Python implementation of JetBrains/swot

Python

LEOPARD-challenge-baseline

Python

rse-sagemaker-shim

Adapts algorithms that implement the Grand Challenge inference API for running in SageMaker

Python

CE-CT_PDAC_AutomaticDetection_nnUnet

Fully automtic pipeline for pancreatic ductal adenocarcinoma (PDAC) detection on contrast enhanced computed tomography (CE-CT) scans

Python

auc23-t2-algorithm-container-rib-ct-fracture

Python

DIAGNijmegen/m-heads

DIAGNijmegen

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