Skip to content

Latest commit

 

History

History
68 lines (44 loc) · 4.58 KB

README.md

File metadata and controls

68 lines (44 loc) · 4.58 KB
Raw

MuSiC-ViT: Multi-task Siamese Convolutional Vision Transformer for Differentiating Change or No-change of Follow-Up Chest X-Rays

Figure_final_main

Directory Architecture

Root

|---------- train.json (You have to create yourself.)

|---------- test.json (You have to create yourself.)

|---------- config.py

|---------- datasets.py

|---------- README.md

|---------- test.py

|---------- train.py

|---------- utils.py

|---------- runs (if you run the train code, it will be made automatically)

|---------- checkpoints (if you run the train code, it will be made automatically)

Train

CUDA_VISIBLE_DEVICES=0 python train.py --msg=train--aug True --lr 6e-5 --batch_size=20 --print_freq=300 --backbone CMT_Ti

Test

CUDA_VISIBLE_DEVICES=0 python test.py --msg=test --batch_size=6 --pretrained checkpoints/2022-08-25_163910_MuSiC_ViT/174048.pth --backbone CMT_Ti

MuSiC-ViT weight link: https://drive.google.com/drive/folders/1TZYdh4ERKBXa_-OH0LWxofGAsGWDc3_H?usp=sharing

CheXpert dataset link: https://drive.google.com/drive/folders/1wD_LI0mPlQWNWS47L44w2guuk4aHNmw-?usp=sharing

Results

The results compared to other various model architectures are shown in the table below.

Model architecture Param (M) Training (h) / Testing (s) Internal validation dataset External validation dataset 1 External validation dataset 2
SPE 　 SEN 　 ACC 　 AUC SPE 　 SEN 　 ACC 　 AUC SPE 　 SEN 　 ACC 　 AUC
CNN Inception-v3 22.84M 31h / 36s 0.790 　 0.568 　 0.679 　 0.732 0.884 　 0.265 　 0.574 　 0.665 0.866 　 0.449 　 0.659 　 0.723
ResNet-50 24.55M 36h / 24s 0.806 　 0.564 　 0.685 　 0.749 0.902 　 0.279 　 0.591 　 0.639 0.828 　 0.453 　 0.642 　 0.721
DenseNet-121 7.48M 32h / 46s 0.752 　 0.595 　 0.674 　 0.722 0.828 　 0.363 　 0.595 　 0.662 0.869 　 0.532 　 0.702 　 0.758
EfficientNet-b3 11.49M 29h / 44s 0.790 　 0.557 　 0.674 　 0.741 0.888 　 0.270 　 0.579 　 0.655 0.884 　 0.506 　 0.696 　 0.655
EfficientNet-v2 21.23M 49h / 48s 0.736 　 0.594 　 0.665 　 0.712 0.726 　 0.437 　 0.581 　 0.649 0.866 　 0.543 　 0.705 　 0.760
ConvNeXt 28.59M 67h / 29s 0.751 　 0.544 　 0.648 　 0.699 0.833 　 0.400 　 0.616 　 0.689 0.914 　 0.415 　 0.666 　 0.736
Transformer ViT-B 88.28M 33h / 17s 0.816 　 0.448 　 0.632 　 0.677 0.902 　 0.200 　 0.551 　 0.618 0.978　 0.102 　 0.542 　 0.633
Swin-v2 28.35M 54h / 37s 0.665 　 0.611 　 0.638 　 0.692 0.842 　 0.369 　 0.595 　 0.637 0.892 　 0.433 　 0.664 　 0.742
MLP-Mixer 32.18M 52h / 24s 0.706 　 0.656 　 0.681 　 0.727 0.754 　0.516　 0.653 　 0.660 0.787 　 0.521 　 0.655 　 0.705
ResMLP 20.21M 56h / 16s 0.704 　 0.649 　 0.677 　 0.724 0.707 　 0.414 　 0.561 　 0.609 0.758 　 0.411 　 0.585 　 0.617
CoaT 11.01M 42h / 38s 0.709 　 0.651 　 0.680 　 0.734 0.795 　 0.335 　 0.565 　 0.609 0.724 　 0.555 　 0.640 　 0.698
CMT-Ti 31.62M 29h / 59s 0.721 　0.682　 0.701 　 0.762 0.795 　 0.488 　0.642　 0.674 0.772 　0.626　 0.700 　 0.757
MuSiC-ViT (ours) 31.81M 39h / 70s 0.817　 0.638 　0.728 　 0.797 0.930　 0.298 　 0.614 　0.784 0.899 　 0.589 　0.745 　 0.858

*Note: number of model parameters (Param);specificity (SPE); sensitivity (SEN); accuracy (ACC); area under receiver operating characteristics curve (AUC); million (M); hours (h); seconds (s)