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A Deep Learning System That Generates Quantitative CT Reports for Diagnosing Pulmonary Tuberculosis
The best model was selected to annotate the spatial location of lesions and classify them into miliary, infiltrative, caseous, tuberculoma and cavitary types simultaneously. Then the Noisy-Or Bayesian function was used to generate an overall infection probability. Finally, a quantitative diagnostic report was exported. The results showed that the recall and precision rates, from the perspective of a single lesion region of PTB, were 85. 9% and 89. 2% respectively.
Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
We found that the real time reverse transcription-polymerase chain reaction (RT-PCR) detection of viral RNA from sputum or nasopharyngeal swab has a relatively low positive rate in the early stage to determine COVID-19 (named by the World Health Organization).
Deep learning to estimate the physical proportion of infected region of lung for COVID-19 pneumonia with CT image set
For the segmented masks of intact lung and infected regions, the best method could achieve 0. 972 and 0. 757 measure in mean Dice similarity coefficient on our test benchmark.
