OXnet: Omni-supervised Thoracic Disease Detection from Chest X-rays

04/07/2021
by   Luyang Luo, et al.
0

Chest X-ray (CXR) is the most typical medical image worldwide to examine various thoracic diseases. Automatically localizing lesions from CXR is a promising way to alleviate radiologists' daily reading burden. However, CXR datasets often have numerous image-level annotations and scarce lesion-level annotations, and more often, without annotations. Thus far, unifying different supervision granularities to develop thoracic disease detection algorithms has not been comprehensively addressed. In this paper, we present OXnet, the first deep omni-supervised thoracic disease detection network to our best knowledge that uses as much available supervision as possible for CXR diagnosis. Besides fully supervised learning, to enable learning from weakly-annotated data, we guide the information from a global classification branch to the lesion localization branch by a dual attention alignment module. To further enhance global information learning, we impose intra-class compactness and inter-class separability with a global prototype alignment module. For unsupervised data learning, we extend the focal loss to be its soft form to distill knowledge from a teacher model. Extensive experiments show the proposed OXnet outperforms competitive methods with significant margins. Further, we investigate omni-supervision under various annotation granularities and corroborate OXnet is a promising choice to mitigate the plight of annotation shortage for medical image diagnosis.

READ FULL TEXT

page 3

page 8

page 13

research
01/25/2021

Weakly Supervised Thoracic Disease Localization via Disease Masks

To enable a deep learning-based system to be used in the medical domain ...
research
01/22/2021

Cross Chest Graph for Disease Diagnosis with Structural Relational Reasoning

Locating lesions is important in the computer-aided diagnosis of X-ray i...
research
06/23/2023

Deep Omni-supervised Learning for Rib Fracture Detection from Chest Radiology Images

Deep learning (DL)-based rib fracture detection has shown promise of pla...
research
04/21/2021

Rethinking annotation granularity for overcoming deep shortcut learning: A retrospective study on chest radiographs

Deep learning has demonstrated radiograph screening performances that ar...
research
10/13/2022

Probabilistic Integration of Object Level Annotations in Chest X-ray Classification

Medical image datasets and their annotations are not growing as fast as ...
research
07/10/2022

Radiomics-Guided Global-Local Transformer for Weakly Supervised Pathology Localization in Chest X-Rays

Before the recent success of deep learning methods for automated medical...
research
08/19/2022

Diagnose Like a Radiologist: Hybrid Neuro-Probabilistic Reasoning for Attribute-Based Medical Image Diagnosis

During clinical practice, radiologists often use attributes, e.g. morpho...

Please sign up or login with your details

Forgot password? Click here to reset