Improved Semantic Segmentation of Tuberculosis—Consistent Findings in Chest X-rays Using Augmented Training of Modal-Ity-Specific U-Net Models with Weak Localizations

Improved Semantic Segmentation of Tuberculosis—Consistent Findings in Chest X-rays Using Augmented Training of Modal-Ity-Specific U-Net Models with Weak Localizations

Deep learning (DL) has drawn tremendous attention for object localization and recognition in both natural and medical images. U-Net segmentation models have demonstrated superior performance compared to conventional hand-crafted feature-based methods. Medical image modality-specific DL models are be...

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Bibliographic Details
Main Authors: Sivaramakrishnan Rajaraman, Les R. Folio, Jane Dimperio, Philip O. Alderson, Sameer K. Antani
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Diagnostics
Subjects:
deep learning
tuberculosis
convolutional neural networks
segmentation
modality-specific knowledge transfer
U-Net
Online Access:https://www.mdpi.com/2075-4418/11/4/616

Internet

https://www.mdpi.com/2075-4418/11/4/616

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