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

  git clone https://github.com/mrdvince/dl4tb.git

Go to the project directory

  cd dl4tb

Install dependencies

Using cpu version of pytorch, if you have a supported GPU for training get that version of pytorch.

  pip install -r requirements.txt

Start the training

    python src/train.py

Available args thanks to hydra

Add the -h flag to check all the available settings and override some.

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

    # <model checkpoint path> modify this to the path of your model
    python src/export.py '+cfg.ckpt_path="<model checkpoint path>"'