Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models
J Zipfel, F Verworner, M Fischer, U Wieland… - Computers & Industrial …, 2023 - Elsevier
Across many industries, visual quality assurance has transitioned from a manual, labor-
intensive, and error-prone task to a fully automated and precise assessment of industrial …
intensive, and error-prone task to a fully automated and precise assessment of industrial …
A multi-stream convolutional neural network for classification of progressive MCI in Alzheimer's disease using structural MRI images
M Ashtari-Majlan, A Seifi… - IEEE Journal of …, 2022 - ieeexplore.ieee.org
Early diagnosis of Alzheimer's disease and its prodromal stage, also known as mild
cognitive impairment (MCI), is critical since some patients with progressive MCI will develop …
cognitive impairment (MCI), is critical since some patients with progressive MCI will develop …
Efficient U-Net architecture with multiple encoders and attention mechanism decoders for brain tumor segmentation
The brain is the center of human control and communication. Hence, it is very important to
protect it and provide ideal conditions for it to function. Brain cancer remains one of the …
protect it and provide ideal conditions for it to function. Brain cancer remains one of the …
Scribble2d5: Weakly-supervised volumetric image segmentation via scribble annotations
Image segmentation using weak annotations like scribbles has gained great attention, since
such annotations are easier to obtain compared to time-consuming and labor-intensive …
such annotations are easier to obtain compared to time-consuming and labor-intensive …
Self-supervised tumor segmentation with sim2real adaptation
This paper targets on self-supervised tumor segmentation. We make the following
contributions:(i) we take inspiration from the observation that tumors are often characterised …
contributions:(i) we take inspiration from the observation that tumors are often characterised …
Afsc: Adaptive fourier space compression for anomaly detection
The primary challenge faced by reconstruction-based anomaly detection (AD) methods is
that neural networks exhibit strong generalization, resulting in a high probability and …
that neural networks exhibit strong generalization, resulting in a high probability and …
LF-SynthSeg: Label-Free Brain Tissue-Assisted Tumor Synthesis and Segmentation
Unsupervised brain tumor segmentation is pivotal in realms of disease diagnosis, surgical
planning, and treatment response monitoring, with the distinct advantage of obviating the …
planning, and treatment response monitoring, with the distinct advantage of obviating the …
Conditional diffusion models for weakly supervised medical image segmentation
Recent advances in denoising diffusion probabilistic models have shown great success in
image synthesis tasks. While there are already works exploring the potential of this powerful …
image synthesis tasks. While there are already works exploring the potential of this powerful …
Unsupervised liver tumor segmentation with pseudo anomaly synthesis
Liver lesion segmentation is a challenging task. Liver lesions often appear as regional
heterogeneity in various shapes and intensities, while collecting a comprehensive dataset …
heterogeneity in various shapes and intensities, while collecting a comprehensive dataset …
Ame-cam: Attentive multiple-exit cam for weakly supervised segmentation on mri brain tumor
Magnetic resonance imaging (MRI) is commonly used for brain tumor segmentation, which
is critical for patient evaluation and treatment planning. To reduce the labor and expertise …
is critical for patient evaluation and treatment planning. To reduce the labor and expertise …