COVID-19

This repo is for the experiment codes.

We regard COVID-19 diagnosis as a multi-classification task and classified to four classes including healthy, COVID-19, other viral pneumonia, bacterial pneumonia. As for our datasets, we use multi-center datasets consisting of data from Main Campus hospital, Optical Valley hospital, Sino-French hospital and 18 hospitals in NCCID.

Train and validation split:

Patients / CTs	N/A (healthy)	COVID-19	Other viral	Bacterial	Total patients / CTs
Main Campus	224 / 727	135 / 922	56 / 250	254 / 934	669 / 2833
Optical Valley	75 / 278	112 / 425	0 / 0	13 / 47	200 / 750
Sino-French	43 / 131	158 / 853	0 / 0	25 / 97	226 / 1081
NCCID	392 / 1491	199 / 654	0 / 0	0 / 0	591 / 2145

Test split:

Patients / CTs	N/A (healthy)	COVID-19	Other viral	Bacterial	Total patients / CTs
Main Campus	58 / 191	34 / 191	19 / 72	50 / 170	103 / 624
Optical Valley	12 / 44	23 / 88	0 / 0	2 / 8	37 / 140
Sino-French	10 / 27	37 / 244	1 / 12	8 / 27	56 / 310
NCCID	235 / 362	90 / 175	0 / 0	0 / 0	345 / 537

Corresponding label to class (four- class classification):

label	name
0	healthy
1	COVID-19
2	other viral pneumonia
3	bacterial pneumonia

Data Preprocess

Codes for data preprocessing are in: ./utils.

The raw CT images we get from hospitals are not exactly what we can feed to network directly. So there are some cleaning operations to be conducted to the initial datasets. The operations are built based on our careful check with the CT images. We find that when the slice numbers of CT are less than 15 and the width or height of CT images are not equal to 512 pixels, the images are usually useless. So that we clip all images' pixels to [-1200, 600], which is a ordinay operation in medical image. Finally, we calculate the mean and std of the whole datasets and then normalize each image.

Model

We ultilize 3D-DenseNet as our baseline model. Before we feed images into network, we find that if we can cover the whole lung in temporal direction, the model behaves much better. Besides, we confim that there is a linear relation between slice thickness and slice numbers. As a result, a sample strategy is proposed as the following pseudo codes said:

if slice z_len <= 80:
    random start index;
    choose image every 1 interval; # if start=0, choose [0,1,2,...,13,14,15]
elif slice z_len <= 160:
    random start index from [10, z_len - 60];
    choose image every 2 interval; # if start=10, choose [10,12,14,...,36,38,40]
else:
    start=random.randrange(20, z_len - 130)
    random start index;
    choose image every 5 interval; # if start=0, choose [20,25,30,...,85,90,95] 

• Resize sequence images to [16,128,128].
• Without augmentation.
• Regulization --- linear scheduler of dropblock (block=5) from prob=0.0 to prob=0.5.
• Optimizer --- torch.optim.SGD(params, lr=0.01, momentum=0.9).
• No bias decay --- weight decay = 4e-5.
• Lr_scheduler ---Warmup and CosineAnnealing.
• Output layer --- FC(features, 4) -> weighted cross entropy of [0.2, 0.2, 0.4, 0.2]
• batch size --- 70.
• Machine Resource --- 2 Tesla V100.

Federated Learning

Due to levels of incompleteness, isolation, and the heterogeneity in the different data resources, the locally trained models exhibited less-than-ideal test performances on other CT sources. To overcome this hurdle, We proposed a federated learning framework to facilitate UCADI, intergrating ethnically diverse cohorts as part of global joint effort on developing a precise and generalized AI diagnostic model. The concrete introduction of federated learning locates at the repo: https://github.com/HUST-EIC-AI-LAB/COVID-19-Fedrated-Learning-Framework.

Results

We use the hospital's name indicates the model trained on the hospital's data resources in the following tables, and 'Federated' means trained with four clients separately having train data from Main Campus, Optical Valley, Sino-French hospital and NCCID based on federated learning framework.

COVID-19 peneumonia identification performance of CNN models on China data(China data means the merged version of test dataset including Main Campus hospital, Optical Valley hospital, Sino-French hospital) and UK data(UK data means the data from 18 hospitals in NCCID) as following:

China data:

	Main Campus	Optical Valley	Sino-French	NCCID	Federated
Sensitivity	0.538	0.973	0.900	0.313	0.973
Specificity	0.926	0.444	0.759	0.907	0.951
AUC	0.840	0.884	0.922	0.745	0.980

UK data:

	Main Campus	Optical Valley	Sino-French	NCCID	Federated
Sensitivity	0.054	0.541	1,999	0.703	0.730
Specificity	0.835	0.626	0.160	0.961	0.942
AUC	0.487	0.647	0.613	0.882	0.894