76 research outputs found
Core-Periphery Principle Guided Redesign of Self-Attention in Transformers
Designing more efficient, reliable, and explainable neural network
architectures is critical to studies that are based on artificial intelligence
(AI) techniques. Previous studies, by post-hoc analysis, have found that the
best-performing ANNs surprisingly resemble biological neural networks (BNN),
which indicates that ANNs and BNNs may share some common principles to achieve
optimal performance in either machine learning or cognitive/behavior tasks.
Inspired by this phenomenon, we proactively instill organizational principles
of BNNs to guide the redesign of ANNs. We leverage the Core-Periphery (CP)
organization, which is widely found in human brain networks, to guide the
information communication mechanism in the self-attention of vision transformer
(ViT) and name this novel framework as CP-ViT. In CP-ViT, the attention
operation between nodes is defined by a sparse graph with a Core-Periphery
structure (CP graph), where the core nodes are redesigned and reorganized to
play an integrative role and serve as a center for other periphery nodes to
exchange information. We evaluated the proposed CP-ViT on multiple public
datasets, including medical image datasets (INbreast) and natural image
datasets. Interestingly, by incorporating the BNN-derived principle (CP
structure) into the redesign of ViT, our CP-ViT outperforms other
state-of-the-art ANNs. In general, our work advances the state of the art in
three aspects: 1) This work provides novel insights for brain-inspired AI: we
can utilize the principles found in BNNs to guide and improve our ANN
architecture design; 2) We show that there exist sweet spots of CP graphs that
lead to CP-ViTs with significantly improved performance; and 3) The core nodes
in CP-ViT correspond to task-related meaningful and important image patches,
which can significantly enhance the interpretability of the trained deep model.Comment: Core-periphery, functional brain networks, Vi
Clinical characteristics of respiratory tract infection caused by Klebsiella pneumoniae in immunocompromised patients: a retrospective cohort study
PurposeWith advancements in medical technology and the growth of an aging society, the number of immunocompromised patients has increased progressively. Klebsiella pneumoniae (K. pneumoniae) is one of the most common opportunistic pathogens, causing a severe disease burden. We aimed to further clarify the differences in respiratory tract K. pneumoniae infections between immunocompromised and immunocompetent populations.MethodsWe retrospectively compared cases of respiratory tract K. pneumoniae infection in immunocompromised and immunocompetent patients admitted to Ruijin Hospital in Shanghai between January 2019 and August 2020 to clarify the differences between the two groups.ResultsWe enrolled 400 immunocompromised patients and 386 immunocompetent patients. Compared to the immunocompetent group, immunocompromised patients were more likely to develop bacteremia and shock and to require mechanical ventilation support during hospitalization. Immunocompromised patients also had a greater probability of polymicrobial infection and a higher rate of antibacterial resistance to carbapenem, which resulted in a higher intensive care unit admission rate, 30-day case fatality rate (CFR), and 6-month CFR. Multivariate analysis indicated that immunocompromised patients with respiratory diseases (odds ratio [OR], 2.189; 95% confidence interval [CI], 1.103-4.344; P = 0.025) and cardiovascular diseases (OR, 2.008; 95% CI, 1.055-3.822; P = 0.034), using mechanical ventilation (OR, 3.982; 95% CI, 2.053-7.722; P = 0.000), or infected with multidrug-resistant K. pneumoniae (OR, 3.870; 95%, 1.577-9.498; P = 0.003) were more likely to have a higher 30-day CFR.ConclusionThe disease burden of K. pneumoniae infection in immunocompromised patients is high. Immunocompromised patients who presented with respiratory diseases and cardiovascular diseases, used mechanical ventilation, or were infected with multidrug-resistant K. pneumoniae experienced a higher 30-day mortality rate
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is
becoming a critical concern. Many approaches have been developed to tackle the
problem through post-hoc analysis, such as explaining how predictions are made
or understanding the meaning of neurons in middle layers. Nevertheless, these
methods can only discover the patterns or rules that naturally exist in models.
In this work, rather than relying on post-hoc schemes, we proactively instill
knowledge to alter the representation of human-understandable concepts in
hidden layers. Specifically, we use a hierarchical tree of semantic concepts to
store the knowledge, which is leveraged to regularize the representations of
image data instances while training deep models. The axes of the latent space
are aligned with the semantic concepts, where the hierarchical relations
between concepts are also preserved. Experiments on real-world image datasets
show that our method improves model interpretability, showing better
disentanglement of semantic concepts, without negatively affecting model
classification performance
Patterns of Lymph Node Metastasis and Optimal Surgical Strategy in Small (≤20 mm) Gastroenteropancreatic Neuroendocrine Tumors
BackgroundRegional lymph node metastasis (LNM) is crucial for planning additional lymphadenectomy, and is directly correlated with poor prognosis in gastroenteropancreatic neuroendocrine tumors (GEP-NETs). However, the patterns of LNM for small (≤20 mm) GEP-NETs remain unclear. This population-based study aimed at evaluating LNM patterns and identifying optimal surgical strategies from the standpoint of lymph node dissemination.MethodsThis retrospective cohort study retrieved data from the Surveillance, Epidemiology, and End Results (SEER) 18 registries database for 17,308 patients diagnosed as having localized well-differentiated GEP-NETs ≤ 20 mm between January 1, 2004, and December 31, 2017. The patterns of LNM were characterized in 6,622 patients who underwent extended resection for adequate lymph node harvest.ResultsOf 6,622 patients with localized small GEP-NETs in the current study, 2,380 (36%) presented with LNM after regional lymphadenectomy. Nodal involvement was observed in approximately 7.4%, 49.1%, 13.6%, 53.7%, 13.8%, 7.8%, and 15.4% of gastric (g-), small intestinal (si-), appendiceal (a-), colonic (c-), rectal (r-), non-functional pancreatic (nfp-), and functional pancreatic (fp-) NETs ≤ 20 mm. Patients with younger age, larger tumor size, and muscularis invasion were more likely to present with LNM. Additional lymphadenectomy conferred a significant survival advantage in NETs (≤10 mm: HR, 0.47; 95% CI, 0.33–0.66; p < 0.001; 11–20 mm: HR, 0.54; 95% CI, 0.34–0.85; p = 0.008) and fp-NETs ≤ 20 mm (HR, 0.08; 95% CI, 0.02–0.36; p = 0.001), as well as g-NETs (HR, 0.39; 95% CI, 0.16–0.96; p = 0.041) and c-NETs of 11–20 mm (HR, 0.07; 95% CI, 0.01–0.48; p = 0.007). Survival benefits of additional lymphadenectomy were not found in a-NETs, r-NETs, and nfp-NETs with a small size.ConclusionsGiven the increased risk for nodal metastasis, primary tumor resection with regional lymphadenectomy is a potential optimal surgical strategy for si-NETs and fp-NETs ≤ 20 mm, as well as g-NETs and c-NETs of 11–20 mm. Local resection is an appropriate and reliable surgical approach for a-NETs, r-NETs, and nfp-NETs ≤ 20 mm
Segment Anything Model (SAM) for Radiation Oncology
In this study, we evaluate the performance of the Segment Anything Model
(SAM) model in clinical radiotherapy. We collected real clinical cases from
four regions at the Mayo Clinic: prostate, lung, gastrointestinal, and head \&
neck, which are typical treatment sites in radiation oncology. For each case,
we selected the OARs of concern in radiotherapy planning and compared the Dice
and Jaccard outcomes between clinical manual delineation, automatic
segmentation using SAM's "segment anything" mode, and automatic segmentation
using SAM with box prompt. Our results indicate that SAM performs better in
automatic segmentation for the prostate and lung regions, while its performance
in the gastrointestinal and head \& neck regions was relatively inferior. When
considering the size of the organ and the clarity of its boundary, SAM displays
better performance for larger organs with clear boundaries, such as the lung
and liver, and worse for smaller organs with unclear boundaries, like the
parotid and cochlea. These findings align with the generally accepted
variations in difficulty level associated with manual delineation of different
organs at different sites in clinical radiotherapy. Given that SAM, a single
trained model, could handle the delineation of OARs in four regions, these
results also demonstrate SAM's robust generalization capabilities in automatic
segmentation for radiotherapy, i.e., achieving delineation of different
radiotherapy OARs using a generic automatic segmentation model. SAM's
generalization capabilities across different regions make it technically
feasible to develop a generic model for automatic segmentation in radiotherapy
SAMAug: Point Prompt Augmentation for Segment Anything Model
This paper introduces SAMAug, a novel visual point augmentation method for
the Segment Anything Model (SAM) that enhances interactive image segmentation
performance. SAMAug generates augmented point prompts to provide more
information about the user's intention to SAM. Starting with an initial point
prompt, SAM produces an initial mask, which is then fed into our proposed
SAMAug to generate augmented point prompts. By incorporating these extra
points, SAM can generate augmented segmentation masks based on both the
augmented point prompts and the initial prompt, resulting in improved
segmentation performance. We conducted evaluations using four different point
augmentation strategies: random sampling, sampling based on maximum difference
entropy, maximum distance, and saliency. Experiment results on the COCO,
Fundus, COVID QUEx, and ISIC2018 datasets show that SAMAug can boost SAM's
segmentation results, especially using the maximum distance and saliency.
SAMAug demonstrates the potential of visual prompt augmentation for computer
vision. Codes of SAMAug are available at github.com/yhydhx/SAMAu
When Brain-inspired AI Meets AGI
Artificial General Intelligence (AGI) has been a long-standing goal of
humanity, with the aim of creating machines capable of performing any
intellectual task that humans can do. To achieve this, AGI researchers draw
inspiration from the human brain and seek to replicate its principles in
intelligent machines. Brain-inspired artificial intelligence is a field that
has emerged from this endeavor, combining insights from neuroscience,
psychology, and computer science to develop more efficient and powerful AI
systems. In this article, we provide a comprehensive overview of brain-inspired
AI from the perspective of AGI. We begin with the current progress in
brain-inspired AI and its extensive connection with AGI. We then cover the
important characteristics for both human intelligence and AGI (e.g., scaling,
multimodality, and reasoning). We discuss important technologies toward
achieving AGI in current AI systems, such as in-context learning and prompt
tuning. We also investigate the evolution of AGI systems from both algorithmic
and infrastructural perspectives. Finally, we explore the limitations and
future of AGI
AGI for Agriculture
Artificial General Intelligence (AGI) is poised to revolutionize a variety of
sectors, including healthcare, finance, transportation, and education. Within
healthcare, AGI is being utilized to analyze clinical medical notes, recognize
patterns in patient data, and aid in patient management. Agriculture is another
critical sector that impacts the lives of individuals worldwide. It serves as a
foundation for providing food, fiber, and fuel, yet faces several challenges,
such as climate change, soil degradation, water scarcity, and food security.
AGI has the potential to tackle these issues by enhancing crop yields, reducing
waste, and promoting sustainable farming practices. It can also help farmers
make informed decisions by leveraging real-time data, leading to more efficient
and effective farm management. This paper delves into the potential future
applications of AGI in agriculture, such as agriculture image processing,
natural language processing (NLP), robotics, knowledge graphs, and
infrastructure, and their impact on precision livestock and precision crops. By
leveraging the power of AGI, these emerging technologies can provide farmers
with actionable insights, allowing for optimized decision-making and increased
productivity. The transformative potential of AGI in agriculture is vast, and
this paper aims to highlight its potential to revolutionize the industry
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