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To construct a deep learning model based on multi-task learning to assist clinicians in differential diagnosis and staging of pneumoconiosis.
The digital chest radiographs of 3 600 patients from an occupational disease hospital in Sichuan Province from 2011 to 2022 were collected, and the full convolution neural network (UNet) was used to segment the lung field. Based on multi-task learning, the multi-task model was constructed using the correlation between tasks.The multi-task model was pre-trained on the ChestX-ray14 dataset, whose backbone network was DenseNet121, and two classifiers were added behind the backbone network. Paired t-test was used to compare the differences in accuracy, precision, sensitivity, and F1 scores between single-task model and multi-task model.
The test set results showed that the differential diagnosis and diagnostic staging performance of the single-task model was about 90% and 77%, respectively. The differential diagnosis and diagnosis staging performance of the multi-task model was about 94% and 86%, which was higher than that of the single-task model about 4% and 9%, respectively. The difference between the evaluation indexes was statistically significant (P < 0.05).
The multi-task model has more advantages than the single-task model and can effectively realize the differential diagnosis and accurate staging of pneumoconiosis and pulmonary tuberculosis.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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