[HTML][HTML] Deep learning for chest X-ray analysis: A survey
E Çallı, E Sogancioglu, B van Ginneken… - Medical Image …, 2021 - Elsevier
Recent advances in deep learning have led to a promising performance in many medical
image analysis tasks. As the most commonly performed radiological exam, chest …
image analysis tasks. As the most commonly performed radiological exam, chest …
Deep learning applications in pulmonary medical imaging: recent updates and insights on COVID-19
Shortly after deep learning algorithms were applied to Image Analysis, and more importantly
to medical imaging, their applications increased significantly to become a trend. Likewise …
to medical imaging, their applications increased significantly to become a trend. Likewise …
Models genesis
Transfer learning from natural images to medical images has been established as one of the
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …
most practical paradigms in deep learning for medical image analysis. To fit this paradigm …
Delving into masked autoencoders for multi-label thorax disease classification
Abstract Vision Transformer (ViT) has become one of the most popular neural architectures
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
due to its simplicity, scalability, and compelling performance in multiple vision tasks …
Segmentation and classification on chest radiography: a systematic survey
T Agrawal, P Choudhary - The Visual Computer, 2023 - Springer
Chest radiography (X-ray) is the most common diagnostic method for pulmonary disorders.
A trained radiologist is required for interpreting the radiographs. But sometimes, even …
A trained radiologist is required for interpreting the radiographs. But sometimes, even …
Label co-occurrence learning with graph convolutional networks for multi-label chest x-ray image classification
Existing multi-label medical image learning tasks generally contain rich relationship
information among pathologies such as label co-occurrence and interdependency, which is …
information among pathologies such as label co-occurrence and interdependency, which is …
Learning fixed points in generative adversarial networks: From image-to-image translation to disease detection and localization
Generative adversarial networks (GANs) have ushered in a revolution in image-to-image
translation. The development and proliferation of GANs raises an interesting question: can …
translation. The development and proliferation of GANs raises an interesting question: can …
Quantifying and leveraging predictive uncertainty for medical image assessment
The interpretation of medical images is a challenging task, often complicated by the
presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest …
presence of artifacts, occlusions, limited contrast and more. Most notable is the case of chest …
A disentangled generative model for disease decomposition in chest x-rays via normal image synthesis
The interpretation of medical images is a complex cognition procedure requiring cautious
observation, precise understanding/parsing of the normal body anatomies, and combining …
observation, precise understanding/parsing of the normal body anatomies, and combining …
Discriminative feature learning for thorax disease classification in chest X-ray images
This paper focuses on the thorax disease classification problem in chest X-ray (CXR)
images. Different from the generic image classification task, a robust and stable CXR image …
images. Different from the generic image classification task, a robust and stable CXR image …