https://github.com/mrdvince/dltb
Computer vision for tuberculosis classification and lung segmentation.
https://github.com/mrdvince/dltb
classification deep-learning diagnosis-tool hpu python3 pytorch segmentation
Last synced: 4 months ago
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Computer vision for tuberculosis classification and lung segmentation.
- Host: GitHub
- URL: https://github.com/mrdvince/dltb
- Owner: mrdvince
- License: apache-2.0
- Created: 2021-12-31T22:24:54.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2022-02-18T12:00:35.000Z (over 3 years ago)
- Last Synced: 2025-03-29T04:47:27.120Z (7 months ago)
- Topics: classification, deep-learning, diagnosis-tool, hpu, python3, pytorch, segmentation
- Language: Jupyter Notebook
- Homepage: https://wandb.ai/droid/dl4tb
- Size: 16.4 MB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# dltb
Using computer vision to classify and tuberculosis and lung segmentantion using Montgomery dataset from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4256233/
From the dataset:
>The MC set has been collected in collaboration with the Department of Health and Human Services, Montgomery County, Maryland, USA. The set contains 138 frontal chest X-rays from Montgomery County’s Tuberculosis screening program, of which 80 are normal cases and 58 are cases with manifestations of TB. The X-rays were captured with a Eureka stationary X-ray machine (CR), and are provided in Portable Network Graphics (PNG) format as 12-bit gray level images. They can also be made available in DICOM format upon request. The size of the X-rays is either 4,020×4,892 or 4,892×4,020 pixels.
# Getting Started
The dataset gets pulled from kaggle using opendatasets if it doesn't exist.
Kaggle dataset link: https://www.kaggle.com/kmader/pulmonary-chest-xray-abnormalities.
1. Login into kaggle, head over to the account and get the API token. This is going to needed by
open datasets to download the dataset.
## Run Locally
Clone the project
```bash
git clone https://github.com/mrdvince/dl4tb.git
```
Go to the project directory
```bash
cd dl4tb
```
Install dependencies
Using cpu version of pytorch, if you have a supported GPU for training get that version of pytorch.
```bash
pip install -r requirements.txt
```
Start the training
```bash
python src/train.py
```
### Available args thanks to [hydra](https://github.com/facebookresearch/hydra)
Add the -h flag to check all the available settings and override some.
```bash
python src/train.py -h
(.env9) ➜ dl4tb git:(master) ✗ python src/train.py -h
Global seed set to 69420
train is powered by Hydra.
== Configuration groups ==
Compose your configuration from those groups (group=option)
== Config ==
Override anything in the config (foo.bar=value)
model:
num_classes: 2
lr: 0.001
data:
data_url: https://drive.google.com/uc?id=1KdpV3M27kV-_QOQOrAentfzZ2tew8YS-&
lung_mask_ds_url: https://www.kaggle.com/kmader/pulmonary-chest-xray-abnormalities
lm_batch_size: 16
cl_batch_size: 64
data_dir: data
zip_file: tb_data.zip
exports_dir: saved/exports
cxr_dir: data/proc_seg/cxr_pngs
mask_dir: data/proc_seg/mask_pngs
lung_mask_raw_dir: data/pulmonary-chest-xray-abnormalities/Montgomery/MontgomerySet/CXR_png
lung_mask_dim: 256
training:
max_epochs: 10
log_every_n_steps: 10
limit_train_batches: 1.0
limit_val_batches: 1.0
save_dir: saved
deterministic: true
device: cpu
num_instances: 1
bucket_cap_mb: 125
cores: 8
model: unet
Powered by Hydra (https://hydra.cc)
Use --hydra-help to view Hydra specific help
```
## Exporting model to onnx and torchscript
```bash
# modify this to the path of your model
python src/export.py '+cfg.ckpt_path=""'
```