![CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention. (arXiv:2401.13049v1 [eess.IV])](data:image/svg+xml,%3Csvg%20xmlns='http://www.w3.org/2000/svg'%20viewBox='0%200%201080%20675'%3E%3C/svg%3E)
by jsendak | Jan 25, 2024 | AI
Advancements in medical imaging and endovascular grafting have facilitated
minimally invasive treatments for aortic diseases. Accurate 3D segmentation of
the aorta and its branches is crucial for interventions, as inaccurate
segmentation can lead to erroneous surgical planning and endograft
construction. Previous methods simplified aortic segmentation as a binary image
segmentation problem, overlooking the necessity of distinguishing between
individual aortic branches. In this paper, we introduce Context Infused
Swin-UNet (CIS-UNet), a deep learning model designed for multi-class
segmentation of the aorta and thirteen aortic branches. Combining the strengths
of Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet adopts
a hierarchical encoder-decoder structure comprising a CNN encoder, symmetric
decoder, skip connections, and a novel Context-aware Shifted Window
Self-Attention (CSW-SA) as the bottleneck block. Notably, CSW-SA introduces a
unique utilization of the patch merging layer, distinct from conventional Swin
transformers. It efficiently condenses the feature map, providing a global
spatial context and enhancing performance when applied at the bottleneck layer,
offering superior computational efficiency and segmentation accuracy compared
to the Swin transformers. We trained our model on computed tomography (CT)
scans from 44 patients and tested it on 15 patients. CIS-UNet outperformed the
state-of-the-art SwinUNetR segmentation model, which is solely based on Swin
transformers, by achieving a superior mean Dice coefficient of 0.713 compared
to 0.697, and a mean surface distance of 2.78 mm compared to 3.39 mm.
CIS-UNet’s superior 3D aortic segmentation offers improved precision and
optimization for planning endovascular treatments. Our dataset and code will be
publicly available.
This article explores the advancements in medical imaging and endovascular grafting that have led to minimally invasive treatments for aortic diseases. The accurate segmentation of the aorta and its branches is crucial for successful interventions, as inaccurate segmentation can result in errors in surgical planning and endograft construction. Previous methods have oversimplified aortic segmentation, neglecting the need to distinguish between individual aortic branches. In response to this limitation, the authors introduce Context Infused Swin-UNet (CIS-UNet), a deep learning model specifically designed for multi-class segmentation of the aorta and thirteen aortic branches. By combining Convolutional Neural Networks (CNNs) and Swin transformers, CIS-UNet achieves superior computational efficiency and segmentation accuracy compared to previous models. The authors trained the model on computed tomography (CT) scans from 44 patients and tested it on 15 patients, demonstrating its outperformance of the state-of-the-art SwinUNetR segmentation model. CIS-UNet’s superior 3D aortic segmentation offers improved precision and optimization for planning endovascular treatments, making it a valuable tool in the field.
Revolutionizing Aortic Segmentation with Context Infused Swin-UNet
Advancements in medical imaging and endovascular grafting have provided groundbreaking opportunities for minimally invasive treatments of aortic diseases. However, the accuracy of 3D segmentation of the aorta and its branches plays a critical role in the success of interventions. Inaccurate segmentation can lead to erroneous surgical planning and endograft construction, jeopardizing patient outcomes.
In the past, aortic segmentation was oversimplified as a binary image segmentation problem, disregarding the significance of distinguishing between individual aortic branches. To address this limitation, a team of researchers introduces Context Infused Swin-UNet (CIS-UNet), a deep learning model specifically designed for multi-class segmentation of the aorta and its thirteen branches.
CIS-UNet combines the power of Convolutional Neural Networks (CNNs) and Swin transformers, creating a hierarchical encoder-decoder structure. The model comprises a CNN encoder, symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) as the bottleneck block.
Notably, CIS-UNet introduces a unique utilization of the patch merging layer within CSW-SA, setting it apart from conventional Swin transformers. This innovative technique efficiently condenses the feature map, providing global spatial context. When applied at the bottleneck layer, it enhances performance, offering superior computational efficiency and segmentation accuracy compared to traditional Swin transformers.
To validate the effectiveness of CIS-UNet, the researchers trained the model on computed tomography (CT) scans obtained from 44 patients. The testing phase involved evaluating CIS-UNet on CT scans from an additional 15 patients. The results demonstrated CIS-UNet’s superiority over the state-of-the-art SwinUNetR segmentation model, which solely relies on Swin transformers.
CIS-UNet achieved an impressive mean Dice coefficient of 0.713, surpassing SwinUNetR’s mean Dice coefficient of 0.697. Furthermore, CIS-UNet outperformed SwinUNetR with a mean surface distance of 2.78 mm compared to 3.39 mm. These results confirm CIS-UNet’s exceptional proficiency in accurately segmenting the 3D aorta, offering improved precision and optimization for planning endovascular treatments.
The researchers have made their dataset and code publicly available. This generous gesture encourages further development and collaboration in the field of aortic segmentation, potentially unlocking new possibilities for future advancements in medical imaging and endovascular grafting.
Accurate segmentation of the aorta and its branches is critical for interventions, and CIS-UNet sets a new standard in achieving exceptional precision and computational efficiency. With its integration of CNNs and Swin transformers, this deep learning model paves the way for enhanced planning and optimized endovascular treatments for patients with aortic diseases.
The advancements in medical imaging and endovascular grafting have revolutionized the field of minimally invasive treatments for aortic diseases. However, accurate segmentation of the aorta and its branches is crucial for successful interventions. In this paper, the authors introduce a deep learning model called Context Infused Swin-UNet (CIS-UNet) that addresses the limitations of previous methods and offers improved multi-class segmentation of the aorta and thirteen aortic branches.
CIS-UNet combines the strengths of Convolutional Neural Networks (CNNs) and Swin transformers, which are known for their ability to capture long-range dependencies in images. The model consists of a hierarchical encoder-decoder structure, with a CNN encoder, symmetric decoder, skip connections, and a novel Context-aware Shifted Window Self-Attention (CSW-SA) as the bottleneck block.
One notable feature of CIS-UNet is the unique utilization of the patch merging layer in the CSW-SA, which efficiently condenses the feature map and provides a global spatial context. This enhances the performance of the model, particularly when applied at the bottleneck layer. The authors demonstrate that CIS-UNet offers superior computational efficiency and segmentation accuracy compared to existing Swin transformer-based models.
To evaluate the performance of CIS-UNet, the authors trained the model on computed tomography (CT) scans from 44 patients and tested it on 15 patients. The results show that CIS-UNet outperforms the state-of-the-art SwinUNetR segmentation model, achieving a higher mean Dice coefficient of 0.713 compared to 0.697, and a lower mean surface distance of 2.78 mm compared to 3.39 mm.
The superior 3D aortic segmentation offered by CIS-UNet has significant implications for planning endovascular treatments. The precision and optimization provided by this model can greatly enhance surgical planning and endograft construction, reducing the risk of erroneous interventions. Furthermore, the authors have made their dataset and code publicly available, which will undoubtedly contribute to further advancements in this field.
In summary, the introduction of CIS-UNet as a deep learning model for multi-class segmentation of the aorta and its branches represents a significant step forward in the field of medical imaging and endovascular grafting. The combination of CNNs and Swin transformers, along with the unique features of CIS-UNet, offer improved accuracy and computational efficiency. This model has the potential to greatly enhance the precision and optimization of minimally invasive treatments for aortic diseases, ultimately benefiting patients and healthcare professionals alike.
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by jsendak | Jan 21, 2024 | AI
The development and integration of knowledge graphs and language models has
significance in artificial intelligence and natural language processing. In
this study, we introduce the BERTologyNavigator — a two-phased system that
combines relation extraction techniques and BERT embeddings to navigate the
relationships within the DBLP Knowledge Graph (KG). Our approach focuses on
extracting one-hop relations and labelled candidate pairs in the first phases.
This is followed by employing BERT’s CLS embeddings and additional heuristics
for relation selection in the second phase. Our system reaches an F1 score of
0.2175 on the DBLP QuAD Final test dataset for Scholarly QALD and 0.98 F1 score
on the subset of the DBLP QuAD test dataset during the QA phase.
The development and integration of knowledge graphs and language models have become crucial in the fields of artificial intelligence (AI) and natural language processing (NLP). In this article, we will discuss the BERTologyNavigator, a groundbreaking two-phased system that combines cutting-edge techniques to navigate the relationships within the DBLP Knowledge Graph (KG).
The Significance of Knowledge Graphs and Language Models
Knowledge graphs play a vital role in organizing and representing information in a structured manner. By capturing and linking entities, attributes, and relationships, knowledge graphs provide a comprehensive view of interconnected data. This interconnectivity allows for efficient data retrieval, analysis, and knowledge discovery.
On the other hand, language models have revolutionized NLP by enabling machines to understand and generate human-like text. BERT (Bidirectional Encoder Representations from Transformers), in particular, has gained significant attention due to its ability to capture the context of a word by considering both its preceding and succeeding words. This contextual understanding greatly enhances the accuracy of language-based tasks such as question answering, text classification, and sentiment analysis.
The BERTologyNavigator: A Two-Phased System
The BERTologyNavigator employs an innovative two-phased approach to navigate the relationships within the DBLP Knowledge Graph.
Phase 1: Relation Extraction
In the first phase, the system focuses on extracting one-hop relations and labeled candidate pairs. This involves identifying key entities and their relationships based on predefined patterns, rules, or algorithms. By extracting these relations, the system establishes an initial understanding of the knowledge graph’s structure.
Phase 2: BERT Embeddings and Relation Selection
The second phase utilizes BERT’s CLS embeddings and additional heuristics for relation selection. BERT’s CLS embeddings provide contextual representations of sentences or paragraphs, enabling the system to capture the nuanced meaning of textual data. By applying these embeddings and heuristics, the BERTologyNavigator enhances its ability to select and navigate relevant relationships within the knowledge graph.
Performance and Future Directions
The performance of the BERTologyNavigator is impressive, achieving an F1 score of 0.2175 on the DBLP QuAD Final test dataset for Scholarly QALD during the navigation phase. Additionally, it obtains an outstanding F1 score of 0.98 on a subset of the DBLP QuAD test dataset during the question answering phase. These results demonstrate the effectiveness of the system in extracting and navigating relationships within the knowledge graph.
The multi-disciplinary nature of the BERTologyNavigator is worth noting. It combines techniques from relation extraction, graph theory, language models, and neural networks to achieve its objectives. This interdisciplinary approach highlights the integration of various AI and NLP concepts and emphasizes the importance of collaboration among different domains.
As for future directions, further enhancements can be made to the BERTologyNavigator. For instance, incorporating advanced entity disambiguation techniques can improve the accuracy of relation extraction. Additionally, exploring ways to incorporate external knowledge sources or ontologies may provide richer context for relationship navigation. Continual fine-tuning and updates to the system will ensure its relevance and effectiveness in an evolving knowledge landscape.
Conclusion
The BERTologyNavigator showcases the potential of combining knowledge graphs and language models for relationship navigation within complex datasets. Its two-phased system, along with the utilization of BERT embeddings, demonstrates impressive performance metrics in both navigation and question answering tasks. Unlocking the full potential of knowledge graphs and language models paves the way for advancements in AI and NLP, and the BERTologyNavigator is a remarkable step in that direction.
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by jsendak | Jan 21, 2024 | Computer Science
Expert Commentary:
The integration of machine learning in the field of medicine has revolutionized diagnostic precision, particularly in the interpretation of complex structures such as the human brain. Brain age estimation techniques have emerged as a valuable tool for diagnosing challenging conditions like Alzheimer’s disease. These techniques heavily rely on three-dimensional Magnetic Resonance Imaging (MRI) scans, and recent studies have highlighted the effectiveness of 3D convolutional neural networks (CNNs) like 3D ResNet.
However, the untapped potential of Vision Transformers (ViTs) in this domain has been limited by the absence of efficient 3D versions. Vision Transformers are well-known for their accuracy and interpretability in various computer vision tasks, but their application to brain age estimation has been hindered by this limitation.
In this paper, the authors propose an innovative adaptation of the ViT model called Triamese-ViT to address the limitations of current approaches. Triamese-ViT combines ViTs from three different orientations to capture 3D information, significantly enhancing accuracy and interpretability. The experimental results on a dataset of 1351 MRI scans demonstrate Triamese-ViT’s superiority over previous methods for brain age estimation, achieved through a Mean Absolute Error (MAE) of 3.84 and strong correlation coefficients with chronological age.
One key innovation introduced by Triamese-ViT is its ability to generate a comprehensive 3D-like attention map synthesized from 2D attention maps of each orientation-specific ViT. This feature brings significant benefits in terms of in-depth brain age analysis and disease diagnosis, offering deeper insights into brain health and the mechanisms of age-related neural changes.
The development of Triamese-ViT marks a crucial step forward in the field of brain age estimation using machine learning techniques. By leveraging the strengths of ViTs and incorporating 3D information, this model has the potential to greatly improve accuracy and interpretability in diagnosing age-related neurodegenerative disorders. Further research should explore the generalizability of the Triamese-ViT model across larger and more diverse datasets, as well as its applicability to other medical imaging tasks beyond brain age estimation.
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by jsendak | Jan 11, 2024 | AI
Log anomaly detection is a key component in the field of artificial
intelligence for IT operations (AIOps). Considering log data of variant
domains, retraining the whole network for unknown domains is inefficient in
real industrial scenarios. However, previous deep models merely focused on
extracting the semantics of log sequences in the same domain, leading to poor
generalization on multi-domain logs. To alleviate this issue, we propose a
unified Transformer-based framework for Log anomaly detection (LogFormer) to
improve the generalization ability across different domains, where we establish
a two-stage process including the pre-training and adapter-based tuning stage.
Specifically, our model is first pre-trained on the source domain to obtain
shared semantic knowledge of log data. Then, we transfer such knowledge to the
target domain via shared parameters. Besides, the Log-Attention module is
proposed to supplement the information ignored by the log-paring. The proposed
method is evaluated on three public and one real-world datasets. Experimental
results on multiple benchmarks demonstrate the effectiveness of our LogFormer
with fewer trainable parameters and lower training costs.
Log anomaly detection is a crucial aspect of artificial intelligence for IT operations, as it allows organizations to identify and address abnormal events in log data. However, existing deep models in this field have primarily focused on extracting the semantics of log sequences within a single domain, which limits their ability to generalize across multiple domains.
In this article, the authors propose a unified Transformer-based framework called LogFormer to address this limitation and improve the generalization ability across different domains. The framework consists of a two-stage process, starting with pre-training on a source domain to obtain shared semantic knowledge of log data. This pre-trained model is then fine-tuned on the target domain using adapter-based tuning, where shared parameters are transferred to leverage the knowledge obtained from the source domain.
One key contribution of LogFormer is the introduction of the Log-Attention module. This module supplements the information ignored by log-pairing, which is a technique commonly used for log analysis. By incorporating the Log-Attention module into the Transformer-based model, LogFormer is able to capture additional information from log data, leading to improved anomaly detection performance.
To evaluate the effectiveness of LogFormer, the authors conducted experiments on three public datasets and one real-world dataset. The experimental results demonstrate that LogFormer outperforms existing methods in terms of both detection accuracy and efficiency. Notably, LogFormer achieves these improvements while utilizing fewer trainable parameters and incurring lower training costs compared to previous approaches.
The multi-disciplinary nature of the concepts presented in this article is worth highlighting. The authors combine techniques from artificial intelligence, particularly deep learning and Transformers, with IT operations and log analysis. By leveraging shared parameters and a two-stage process, LogFormer demonstrates the potential for cross-domain generalization in log anomaly detection tasks.
Moving forward, there are several avenues for further exploration in this field. Firstly, it would be valuable to investigate how LogFormer performs on a broader range of domains beyond those considered in the experiments. Additionally, exploring the use of different pre-training techniques, such as self-supervised learning or unsupervised representation learning, could further enhance the generalization abilities of LogFormer. Furthermore, considering the ever-evolving nature of log data in IT operations, ongoing research should focus on developing techniques that can adapt and update the model’s knowledge to stay relevant in dynamic environments. Overall, LogFormer represents a significant step forward in log anomaly detection, showcasing the potential benefits of multi-domain generalization and offering promising directions for future research.
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by jsendak | Jan 10, 2024 | AI
Parameter-efficient tuning (PET) methods such as LoRA, Adapter, and Visual
Prompt Tuning (VPT) have found success in enabling adaptation to new domains by
tuning small modules within a transformer model. However, the number of domains
encountered during test time can be very large, and the data is usually
unlabeled. Thus, adaptation to new domains is challenging; it is also
impractical to generate customized tuned modules for each such domain. Toward
addressing these challenges, this work introduces PLUTO: a Plug-and-pLay
modUlar Test-time domain adaptatiOn strategy. We pre-train a large set of
modules, each specialized for different source domains, effectively creating a
“module store”. Given a target domain with few-shot unlabeled data, we
introduce an unsupervised test-time adaptation (TTA) method to (1) select a
sparse subset of relevant modules from this store and (2) create a weighted
combination of selected modules without tuning their weights. This
plug-and-play nature enables us to harness multiple most-relevant source
domains in a single inference call. Comprehensive evaluations demonstrate that
PLUTO uniformly outperforms alternative TTA methods and that selecting $leq$5
modules suffice to extract most of the benefit. At a high level, our method
equips pre-trained transformers with the capability to dynamically adapt to new
domains, motivating a new paradigm for efficient and scalable domain
adaptation.
PLUTO: A Plug-and-Play Modular Test-Time Domain Adaptation Strategy
Domain adaptation is a crucial task in natural language processing, especially when dealing with new and unlabeled domains. Parameter-efficient tuning (PET) methods like LoRA, Adapter, and Visual Prompt Tuning (VPT) have shown promise in enabling adaptation to new domains by fine-tuning small modules within a transformer model. However, these methods have limitations when the number of domains encountered during test time is large and data is unlabeled.
To address these challenges, researchers have proposed a new strategy called PLUTO. The goal of PLUTO is to create a plug-and-play modular test-time domain adaptation approach that overcomes the limitations of existing methods. The key idea behind PLUTO is to pre-train a large set of modules, each specialized for different source domains, effectively creating a “module store”.
When faced with a target domain with few-shot unlabeled data, PLUTO uses an unsupervised test-time adaptation (TTA) method. This method has two main steps: (1) selecting a sparse subset of relevant modules from the pre-trained module store and (2) creating a weighted combination of these selected modules without tuning their weights. This plug-and-play nature allows PLUTO to leverage multiple relevant source domains in a single inference call.
The results of comprehensive evaluations show that PLUTO consistently outperforms alternative TTA methods. Surprisingly, the experiments reveal that selecting as few as five modules is enough to extract most of the benefit. This means that PLUTO is both efficient and scalable in real-world scenarios with a large number of domains.
At a high level, the PLUTO method equips pre-trained transformers with the capability to dynamically adapt to new domains. This is a significant advancement that motivates a new paradigm for efficient and scalable domain adaptation. The multi-disciplinary nature of the concepts used in PLUTO, combining unsupervised learning, module selection, and weighted combination, demonstrates the importance of integrating different research areas to solve complex natural language processing challenges.
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![CIS-UNet: Multi-Class Segmentation of the Aorta in Computed Tomography Angiography via Context-Aware Shifted Window Self-Attention. (arXiv:2401.13049v1 [eess.IV])](https://qubixity.net/wp-content/uploads/2024/01/cis-unet-multi-class-segmentation-of-the-aorta-in-computed-tomography-angiography-via-context-aware-shifted-window-self-attention-arxiv2401-13049v1-eess-iv-1080x675.jpg)
