26 research outputs found
K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment
The problem of how to assess cross-modality medical image synthesis has been
largely unexplored. The most used measures like PSNR and SSIM focus on
analyzing the structural features but neglect the crucial lesion location and
fundamental k-space speciality of medical images. To overcome this problem, we
propose a new metric K-CROSS to spur progress on this challenging problem.
Specifically, K-CROSS uses a pre-trained multi-modality segmentation network to
predict the lesion location, together with a tumor encoder for representing
features, such as texture details and brightness intensities. To further
reflect the frequency-specific information from the magnetic resonance imaging
principles, both k-space features and vision features are obtained and employed
in our comprehensive encoders with a frequency reconstruction penalty. The
structure-shared encoders are designed and constrained with a similarity loss
to capture the intrinsic common structural information for both modalities. As
a consequence, the features learned from lesion regions, k-space, and
anatomical structures are all captured, which serve as our quality evaluators.
We evaluate the performance by constructing a large-scale cross-modality
neuroimaging perceptual similarity (NIRPS) dataset with 6,000 radiologist
judgments. Extensive experiments demonstrate that the proposed method
outperforms other metrics, especially in comparison with the radiologists on
NIRPS
EasyNet: An Easy Network for 3D Industrial Anomaly Detection
3D anomaly detection is an emerging and vital computer vision task in
industrial manufacturing (IM). Recently many advanced algorithms have been
published, but most of them cannot meet the needs of IM. There are several
disadvantages: i) difficult to deploy on production lines since their
algorithms heavily rely on large pre-trained models; ii) hugely increase
storage overhead due to overuse of memory banks; iii) the inference speed
cannot be achieved in real-time. To overcome these issues, we propose an easy
and deployment-friendly network (called EasyNet) without using pre-trained
models and memory banks: firstly, we design a multi-scale multi-modality
feature encoder-decoder to accurately reconstruct the segmentation maps of
anomalous regions and encourage the interaction between RGB images and depth
images; secondly, we adopt a multi-modality anomaly segmentation network to
achieve a precise anomaly map; thirdly, we propose an attention-based
information entropy fusion module for feature fusion during inference, making
it suitable for real-time deployment. Extensive experiments show that EasyNet
achieves an anomaly detection AUROC of 92.6% without using pre-trained models
and memory banks. In addition, EasyNet is faster than existing methods, with a
high frame rate of 94.55 FPS on a Tesla V100 GPU
Deep Industrial Image Anomaly Detection: A Survey
The recent rapid development of deep learning has laid a milestone in
industrial Image Anomaly Detection (IAD). In this paper, we provide a
comprehensive review of deep learning-based image anomaly detection techniques,
from the perspectives of neural network architectures, levels of supervision,
loss functions, metrics and datasets. In addition, we extract the new setting
from industrial manufacturing and review the current IAD approaches under our
proposed our new setting. Moreover, we highlight several opening challenges for
image anomaly detection. The merits and downsides of representative network
architectures under varying supervision are discussed. Finally, we summarize
the research findings and point out future research directions. More resources
are available at
https://github.com/M-3LAB/awesome-industrial-anomaly-detection
IM-IAD: Industrial Image Anomaly Detection Benchmark in Manufacturing
Image anomaly detection (IAD) is an emerging and vital computer vision task
in industrial manufacturing (IM). Recently many advanced algorithms have been
published, but their performance deviates greatly. We realize that the lack of
actual IM settings most probably hinders the development and usage of these
methods in real-world applications. As far as we know, IAD methods are not
evaluated systematically. As a result, this makes it difficult for researchers
to analyze them because they are designed for different or special cases. To
solve this problem, we first propose a uniform IM setting to assess how well
these algorithms perform, which includes several aspects, i.e., various levels
of supervision (unsupervised vs. semi-supervised), few-shot learning, continual
learning, noisy labels, memory usage, and inference speed. Moreover, we
skillfully build a comprehensive image anomaly detection benchmark (IM-IAD)
that includes 16 algorithms on 7 mainstream datasets with uniform settings. Our
extensive experiments (17,017 in total) provide in-depth insights for IAD
algorithm redesign or selection under the IM setting. Next, the proposed
benchmark IM-IAD gives challenges as well as directions for the future. To
foster reproducibility and accessibility, the source code of IM-IAD is uploaded
on the website, https://github.com/M-3LAB/IM-IAD
Real3D-AD: A Dataset of Point Cloud Anomaly Detection
High-precision point cloud anomaly detection is the gold standard for
identifying the defects of advancing machining and precision manufacturing.
Despite some methodological advances in this area, the scarcity of datasets and
the lack of a systematic benchmark hinder its development. We introduce
Real3D-AD, a challenging high-precision point cloud anomaly detection dataset,
addressing the limitations in the field. With 1,254 high-resolution 3D items
from forty thousand to millions of points for each item, Real3D-AD is the
largest dataset for high-precision 3D industrial anomaly detection to date.
Real3D-AD surpasses existing 3D anomaly detection datasets available regarding
point cloud resolution (0.0010mm-0.0015mm), 360 degree coverage and perfect
prototype. Additionally, we present a comprehensive benchmark for Real3D-AD,
revealing the absence of baseline methods for high-precision point cloud
anomaly detection. To address this, we propose Reg3D-AD, a registration-based
3D anomaly detection method incorporating a novel feature memory bank that
preserves local and global representations. Extensive experiments on the
Real3D-AD dataset highlight the effectiveness of Reg3D-AD. For reproducibility
and accessibility, we provide the Real3D-AD dataset, benchmark source code, and
Reg3D-AD on our website:https://github.com/M-3LAB/Real3D-AD
Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore
Xie G, Wang J, Liu J, Jin Y, Zheng F. Pushing the Limits of Fewshot Anomaly Detection in Industry Vision: Graphcore. Presented at the The Eleventh International Conference on Learning Representations (ICLR 2023)
Interaction and Coordination among Nuclear Power Plants, Power Grids and Their Protection Systems
Nuclear power plants (NPPs) have recently undergone rapid development in China. To improve the performance of both NPPs and grids during adverse conditions, a precise understanding of the coordination between NPPs and grids is required. Therefore, a new mathematical model with reasonable accuracy and reduced computational complexity is developed. This model is applicable to the short, mid, and long-term dynamic simulation of large-scale power systems. The effectiveness of the model is verified by using an actual NPP full-scope simulator as a reference. Based on this model, the interaction and coordination between NPPs and grids under the conditions of over-frequency, under-frequency and under-voltage are analyzed, with special stress applied to the effect of protection systems on the safe operation of both NPPs and power grids. Finally, the coordinated control principles and schemes, together with the recommended protection system values, are proposed for both NPPs and grids. These results show that coordination between the protection systems of NPPs and power networks is a crucial factor in ensuring the safe and stable operation of both NPPs and grids. The results can be used as a reference for coordination between NPPs and grids, as well as for parameter optimization of grid-related generator protection of NPPs
Direct synthesis of methane from CO2/H2O in an oxygen-ion conducting solid oxide electrolyser
Synthetic fuels produced from CO2/H2O are an attractive alternative energy carrier. Here we demonstrate a novel strategy to electrochemically convert CO2/H2O into hydrocarbon in a single step in an oxygen-ion conducting solid oxide electrolyser. Methane was directly synthesized in an efficient electrolyser with configuration of (anode) (La0.8Sr0.2)0.95MnO3−δ/YSZ/La0.2Sr0.8TiO3+δ (cathode) by combining coelectrolysis of CO2/H2O and in situ Fischer–Tropsch-type synthesis. We demonstrate a high Faradaic yield of CO/H2 and lower methane yield, which shows that the limit on conversion efficiency comes from the heterogeneous catalysis process. Electrochemical results also show that the electrochemical reduction of La0.2Sr0.8TiO3+δ cathode is the main process at low electrical voltages while the coelectrolysis is the main process at high voltages
FedMed-ATL: Misaligned Unpaired Brain Image Synthesis via Affine Transform Loss
The existence of completely aligned and paired multi-modal neuroimaging data
has proved its effectiveness in the diagnosis of brain diseases. However,
collecting the full set of well-aligned and paired data is impractical, since
the practical difficulties may include high cost, long time acquisition, image
corruption, and privacy issues. Previously, the misaligned unpaired
neuroimaging data (termed as MUD) are generally treated as noisy label.
However, such a noisy label-based method fail to accomplish well when
misaligned data occurs distortions severely. For example, the angle of rotation
is different. In this paper, we propose a novel federated self-supervised
learning (FedMed) for brain image synthesis. An affine transform loss (ATL) was
formulated to make use of severely distorted images without violating privacy
legislation for the hospital. We then introduce a new data augmentation
procedure for self-supervised training and fed it into three auxiliary heads,
namely auxiliary rotation, auxiliary translation and auxiliary scaling heads.
The proposed method demonstrates the advanced performance in both the quality
of our synthesized results under a severely misaligned and unpaired data
setting, and better stability than other GAN-based algorithms. The proposed
method also reduces the demand for deformable registration while encouraging to
leverage the misaligned and unpaired data. Experimental results verify the
outstanding performance of our learning paradigm compared to other
state-of-the-art approaches.Comment: arXiv admin note: text overlap with arXiv:2201.0895