85 research outputs found
Silk Fibroin/Polyvinyl Pyrrolidone Interpenetrating Polymer Network Hydrogels
Silk fibroin hydrogel is an ideal model as biomaterial matrix due to its excellent biocompatibility and used in the field of medical polymer materials. Nevertheless, native fibroin hydrogels show poor transparency and resilience. To settle these drawbacks, an interpenetrating network (IPN) of hydrogels are synthesized with changing ratios of silk fibroin/N-Vinyl-2-pyrrolidonemixtures that crosslink by H2O2 and horseradish peroxidase. Interpenetrating polymer network structure can shorten the gel time and the pure fibroin solution gel time for more than a week. This is mainly due to conformation from the random coil to the β-sheet structure changes of fibroin. Moreover, the light transmittance of IPN hydrogel can be as high as more than 97% and maintain a level of 90% within a week. The hydrogel, which mainly consists of random coil, the apertures inside can be up to 200 μm. Elastic modulus increases during the process of gelation. The gel has nearly 95% resilience under the compression of 70% eventually, which is much higher than native fibroin gel. The results suggest that the present IPN hydrogels have excellent mechanical properties and excellent transparency.This work was supported by The National Key Research and Development Program of China
(Grant No. 2017YFC1103602), National Natural Science Foundation of China (Grant No. 51373114, 51741301),
PAPD and Nature Science Foundation of Jiangsu, China (Grant No. BK20171239, BK20151242).info:eu-repo/semantics/publishedVersio
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
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
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
Artificial General Intelligence for Medical Imaging
In this review, we explore the potential applications of Artificial General
Intelligence (AGI) models in healthcare, focusing on foundational Large
Language Models (LLMs), Large Vision Models, and Large Multimodal Models. We
emphasize the importance of integrating clinical expertise, domain knowledge,
and multimodal capabilities into AGI models. In addition, we lay out key
roadmaps that guide the development and deployment of healthcare AGI models.
Throughout the review, we provide critical perspectives on the potential
challenges and pitfalls associated with deploying large-scale AGI models in the
medical field. This comprehensive review aims to offer insights into the future
implications of AGI in medical imaging, healthcare and beyond
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
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention
due to their unparalleled performance in language processing. Despite
demonstrating impressive capability in various open-domain tasks, their
adequacy in highly specific fields like radiology remains untested. Radiology
presents unique linguistic phenomena distinct from open-domain data due to its
specificity and complexity. Assessing the performance of large language models
(LLMs) in such specific domains is crucial not only for a thorough evaluation
of their overall performance but also for providing valuable insights into
future model design directions: whether model design should be generic or
domain-specific. To this end, in this study, we evaluate the performance of
ChatGPT/GPT-4 on a radiology NLI task and compare it to other models fine-tuned
specifically on task-related data samples. We also conduct a comprehensive
investigation on ChatGPT/GPT-4's reasoning ability by introducing varying
levels of inference difficulty. Our results show that 1) GPT-4 outperforms
ChatGPT in the radiology NLI task; 2) other specifically fine-tuned models
require significant amounts of data samples to achieve comparable performance
to ChatGPT/GPT-4. These findings demonstrate that constructing a generic model
that is capable of solving various tasks across different domains is feasible
Mesoporous bioactive glass surface modified poly(lactic-co-glycolic acid) electrospun fibrous scaffold for bone regeneration
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