https://github.com/borda/kaggle_vol-3d-classify

Predict the status of a genetic biomarker important for brain cancer treatment
https://github.com/borda/kaggle_vol-3d-classify

3d brain image-classification radiology

Last synced: 10 months ago
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Predict the status of a genetic biomarker important for brain cancer treatment

• Host: GitHub
• URL: https://github.com/borda/kaggle_vol-3d-classify
• Owner: Borda
• License: mit
• Created: 2021-08-12T16:20:34.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2024-12-01T15:04:47.000Z (over 1 year ago)
• Last Synced: 2025-01-03T05:48:10.127Z (over 1 year ago)
• Topics: 3d, brain, image-classification, radiology
• Language: Jupyter Notebook
• Homepage: https://borda.github.io/kaggle_vol-3D-classify
• Size: 1.11 MB
• Stars: 38
• Watchers: 2
• Forks: 4
• Open Issues: 2
• Metadata Files:
  • Readme: README.md
  • License: LICENSE
  • Codeowners: .github/CODEOWNERS

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README

          
# Kaggle competitions on 3D volumes

[![CI complete testing](https://github.com/Borda/kaggle_vol-3D-classify/actions/workflows/ci_testing.yml/badge.svg?branch=main&event=push)](https://github.com/Borda/kaggle_vol-3D-classify/actions/workflows/ci_testing.yml)

[![codecov](https://codecov.io/gh/Borda/kaggle_vol-3D-classify/branch/main/graph/badge.svg?token=bxqTQDXHvU)](https://codecov.io/gh/Borda/kaggle_vol-3D-classify)

[![pre-commit.ci status](https://results.pre-commit.ci/badge/github/Borda/kaggle_vol-3D-classify/main.svg)](https://results.pre-commit.ci/latest/github/Borda/kaggle_vol-3D-classify/main)

## Kaggle: [Cervical Spine Fracture Detection](https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection)

The goal is prediction fracture in whole neck 3D scans. The organizers provide 2k cases with partially annotated fractures as bounding boxes or/and pixel-wise segmentations.

![Sample brain visual](./assets/neck3D_spl.png)

## Kaggle: [Brain Tumor Radiogenomic Classification](https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification)

The goal of this challenge is to Predict the status of a genetic biomarker important for brain cancer treatment.

![Sample brain visual](./assets/brain3D_spl1.png)

  With interpolation in Z dimension as it happens it is quite sparse

![Sample brain visual](./assets/brain3D_spl2.png)

Each independent case has a dedicated folder identified by a five-digit number.

Within each of these “case” folders, there are four sub-folders, each of them corresponding to each of the structural multi-parametric MRI (mpMRI) scans, in DICOM format.

The exact mpMRI scans included are:

- **FLAIR**: Fluid Attenuated Inversion Recovery

- **T1w**: T1-weighted pre-contrast

- **T1Gd**: T1-weighted post-contrast

- **T2**: T2-weighted

The labels/targets are `MGMT_value`:

![Label distribution](./assets/labels.png)

## Experimentation

### install this tooling

A simple way how to use this basic functions:

```bash

! pip install https://github.com/Borda/kaggle_vol-3D-classify/archive/refs/heads/main.zip

```

### run notebooks in Kaggle

- [🧠 Brain Tumor Classif. ~ Lightning⚡EfficientNet3D](https://www.kaggle.com/jirkaborovec/brain-tumor-classif-lightning-efficientnet3d)

- [🧠 Brain Tumor Classif. ~ Lightning⚡MONAI-ResNet3D](https://www.kaggle.com/jirkaborovec/brain-tumor-classif-lightning-monai-resnet3d)

### local notebooks

- [Brain tumor classification meets PT-Lightning and MONAI EfficientNet3D](notebooks/Brain-tumor-classif_PT-Lightning_EfficientNet3D.ipynb)

- [Brain tumor classification meets PT-Lightning and pre-trained ResNet3D](notebooks/Brain-tumor-classif_PT-Lightning_ResNet3D.ipynb)

### some results

Training progress with EfficientNet3D with training for 10 epochs > over 96% validation accuracy:

![Training process](./assets/metrics.png)