WIDINet: A diagnostic model for staging pneumoconiosis based on data expansion and KL entropy judgement

Pneumoconiosis X-Ray Chest X-ray Dataset

We split Pneumoconiosis X-Ray Chest X-ray Dataset to simplify the input of annotations, we generate [train list]and [test list]. Each line is composed of the image name and the corresponding labels like below:

04000251_032.png 0 1 1 0 0 1 0 0 0 1 0 0 0 0

If the image is positive with one class, the corresponding bit is 1, otherwise is 0.

Training ARMGan

The command is following. Please fill in the blanks with your own paths.

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 \
  --master_port=8898 train_ARMGan.py \
  --size 256 \
  --batch 8 \
  --lr 0.001 \
  --trlist data/trainval_list.txt \
  --tslist data/test_list.txt \
  --wandb \
  --proj_name lsae \
  [XRC_PATH] [XRC_Mask_PATH]

Generate analysis of results

Training MKTransformer

The command is following. Please fill in the blanks with your own paths. Before running, you need to download the pretrained_lsae.pt, and put it in the directory saved_ckpts.

CUDA_VISIBLE_DEVICES=0 python train_MKTransformer.py \
  --path [XCR_PATH] \
  --batch 96 \
  --iter 35000 \
  --lr 0.01 \
  --lr_steps 26000 30000 \
  --trlist data/priori_list.txt \
  --tslist data/test_list.txt \
  --enc_ckpt saved_ckpts/pretrained_MKTransformer.pt \
  --wandb