Deep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNet
Background Diagnosis of rib fractures plays an important role in identifying trauma severity.
However, quickly and precisely identifying the rib fractures in a large number of CT images …
However, quickly and precisely identifying the rib fractures in a large number of CT images …
Learning black-box attackers with transferable priors and query feedback
This paper addresses the challenging black-box adversarial attack problem, where only
classification confidence of a victim model is available. Inspired by consistency of visual …
classification confidence of a victim model is available. Inspired by consistency of visual …
Implicitatlas: learning deformable shape templates in medical imaging
Deep implicit shape models have become popular in the computer vision community at
large but less so for biomedical applications. This is in part because large training …
large but less so for biomedical applications. This is in part because large training …
SATr: Slice attention with transformer for universal lesion detection
Abstract Universal Lesion Detection (ULD) in computed tomography plays an essential role
in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input …
in computer-aided diagnosis. Promising ULD results have been reported by multi-slice-input …
Recist-induced reliable learning: Geometry-driven label propagation for universal lesion segmentation
Automatic universal lesion segmentation (ULS) from Computed Tomography (CT) images
can ease the burden of radiologists and provide a more accurate assessment than the …
can ease the burden of radiologists and provide a more accurate assessment than the …
RPLHR-CT dataset and transformer baseline for volumetric super-resolution from CT scans
In clinical practice, anisotropic volumetric medical images with low through-plane resolution
are commonly used due to short acquisition time and lower storage cost. Nevertheless, the …
are commonly used due to short acquisition time and lower storage cost. Nevertheless, the …
Asymmetric 3d context fusion for universal lesion detection
Modeling 3D context is essential for high-performance 3D medical image analysis. Although
2D networks benefit from large-scale 2D supervised pretraining, it is weak in capturing 3D …
2D networks benefit from large-scale 2D supervised pretraining, it is weak in capturing 3D …
Fairness in medical image analysis and healthcare: A literature survey
Machine learning-enabled medical imaging analysis has become a vital part of the
automatic diagnosis system. However, machine learning, especially deep learning models …
automatic diagnosis system. However, machine learning, especially deep learning models …
Advancing 3D medical image analysis with variable dimension transform based supervised 3D pre-training
The difficulties in both data acquisition and annotation substantially restrict the sample sizes
of training datasets for 3D medical imaging applications. Therefore, it is non-trivial to build …
of training datasets for 3D medical imaging applications. Therefore, it is non-trivial to build …
From single to universal: tiny lesion detection in medical imaging
Y Zhang, Y Mao, X Lu, X Zou, H Huang, X Li… - Artificial Intelligence …, 2024 - Springer
Accurate and automatic detection of tiny lesions in medical imaging plays a critical role in
comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous …
comprehensive cancer diagnosis, staging, treatment, follow-up, and prognosis. Numerous …