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ADNet

This repository contains PyTorch-based implementation for "Attentional Decoder Networks for Chest X-ray Image Recognition on High-resolution Features." Our ADNet aims to enhance chest X-ray performance via a lightweight attentional decoder network and harmonic magnitude transforms. We provide model, training, and evaluation (supervised, k-NN classifier) code as well as pre-trained weight in the hope that researchers and practitioners widely use our works effortlessly.


Method Overview of Attentionl Decoder Network

Tutorial

1️⃣ [Inference Only]

  1. Copy mecla/model/convnext.py into your project root folder.

  2. Install timm==0.6.2 and run the following code snippets. We provide the following models with checkpoints.

    • Model Name patterns: [resnet50|convnext_tiny]_ADNet_[Dataset Name]
    • Dataset Name: IN1K, IN1K_NIH, IN1K_MIMIC, IN1K_CheXpert, ALL, ALL_NIH, ALL_MIMIC, ALL_CheXpert.
    !wget -nc https://raw.githubusercontent.com/Lab-LVM/ADNet/main/mecla/model/convnext.py
!wget -nc https://raw.githubusercontent.com/Lab-LVM/ADNet/main/misc/sample.png
import convnext
from timm import create_model
from PIL import Image
from torchvision.transforms import Compose, Resize, CenterCrop, Normalize, ToTensor, Grayscale

img = Image.open('sample.png')
x = Compose([
    Grayscale(3), Resize(472, 3), CenterCrop(448), ToTensor(), 
    Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
])(img)
model = create_model('convnext_tiny_ADNet_ALL_NIH', num_classes=14, pretrained=True)
model.eval()
y = model(x.unsqueeze(0)).argmax()
print(y)
# output: tensor(1) (cardiomegaly)

2️⃣ [Train]

  1. Clone this repository and install the dependency.

    git clone https://github.com/Lab-LVM/ADNet
pip install -r requirements.txt

  2. Download chest X-ray datasets and change data_dir in config/train.json .

    "data_dir": {
  "chexpert": "/path/to/CheXpert-v1.0-small/",
  "nihchest": "/path/to/nih/",
  "mimic": "/path/to/mimic-cxr/",
}

    You can download each dataset using the provided URLs, and the dataset directory looks like the below:

    Tip.

    • We resize images to 384x384 resolution using misc/resize_chexpert.py.
    • We split MIMIC-CXR using CheXclusion protocols. You can download the test patient ID lists from here. Specifically, we upload our dataset preparation guideline for mimix-cxr to here.
    # 1. CheXpert: https://stanfordmlgroup.github.io/competitions/chexpert/
CheXpert-v1.0-small
├── train
├── train.csv
├── valid
└── valid.csv

# 2. MIMIC-CXR: https://physionet.org/content/mimic-cxr/2.0.0/
mimic-cxr
├── files
├── img_384
├── test_x.json
├── test_y.json
├── train_x.json
└── train_y.json

# 3. https://www.kaggle.com/datasets/nih-chest-xrays/data
nih
├── Data_Entry_2017.csv
├── img_384
├── test_list.txt
└── train_val_list.txt

  3. Run the following command.

    • Download a pre-trained checkpoint and modify the checkpoint path of model_weight in config/train.json. You can choose a checkpoint depending on pre-trained dataset types: ImageNet or ALL (NIH+MIMIC+CheXpert).

      No Backbone IN1K ALL
      1 ResNet50 ckpt ckpt log
      2 ConvNeXt-T ckpt ckpt log

    • See more training scripts in script/supervised/.

    • See available training settings in config/train.json.

    • Add --use-wandb for watching progress in wandb.

    # pattern: python3 train.py -c [GPU Device] -s [Setting Name] -m [Model Name]
python3 train.py -s nihchest -c 1 -m convnext_tiny_ADNet_IN1K

Experiment Result

We provide experiment results with pre-trained checkpoints.

Pre-train Data Fine-tune Data Backbone Image AUROC Download
IN1K NIH ResNet50 448 83.5
IN1K NIH ConvNeXt-T 448 83.8 ckpt log
IN1K MIMIC ResNet50 256 84.5
IN1K MIMIC ConvNeXt-T 416 85.3 ckpt log
IN1K CheXpert ResNet50 256 90.6
ALL NIH ResNet50 448 84.1 ckpt log
ALL NIH ConvNeXt-T 448 84.4 ckpt log
ALL MIMIC ResNet50 448 85.2 ckpt log
ALL MIMIC ConvNeXt-T 448 85.6 ckpt log

Note

  • In the pre-train dataset column, ALL means a dataset combined by NIH, MIMIC, and CheXpert using misc/combine_datasets.py.
  • We run our experiments on Ubuntu 18.04 LTS, CUDA 11.03, and a single NVIDIA RTX 3090 24GB GPU.

Acknowledgment

Our code is based on the TIMM library.