222 research outputs found
MedEdit: Model Editing for Medical Question Answering with External Knowledge Bases
Large Language Models (LLMs), although powerful in general domains, often
perform poorly on domain-specific tasks like medical question answering (QA).
Moreover, they tend to function as "black-boxes," making it challenging to
modify their behavior. Addressing this, our study delves into model editing
utilizing in-context learning, aiming to improve LLM responses without the need
for fine-tuning or retraining. Specifically, we propose a comprehensive
retrieval strategy to extract medical facts from an external knowledge base,
and then we incorporate them into the query prompt for the LLM. Focusing on
medical QA using the MedQA-SMILE dataset, we evaluate the impact of different
retrieval models and the number of facts provided to the LLM. Notably, our
edited Vicuna model exhibited an accuracy improvement from 44.46% to 48.54%.
This work underscores the potential of model editing to enhance LLM
performance, offering a practical approach to mitigate the challenges of
black-box LLMs.Comment: 6 page
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Participant recruitment based on unstructured medical texts such as clinical
notes and radiology reports has been a challenging yet important task for the
cohort establishment in clinical research. Recently, Large Language Models
(LLMs) such as ChatGPT have achieved tremendous success in various downstream
tasks thanks to their promising performance in language understanding,
inference, and generation. It is then natural to test their feasibility in
solving the cohort recruitment task, which involves the classification of a
given paragraph of medical text into disease label(s). However, when applied to
knowledge-intensive problem settings such as medical text classification, where
the LLMs are expected to understand the decision made by human experts and
accurately identify the implied disease labels, the LLMs show a mediocre
performance. A possible explanation is that, by only using the medical text,
the LLMs neglect to use the rich context of additional information that
languages afford. To this end, we propose to use a knowledge graph as auxiliary
information to guide the LLMs in making predictions. Moreover, to further boost
the LLMs adapt to the problem setting, we apply a chain-of-thought (CoT) sample
selection strategy enhanced by reinforcement learning, which selects a set of
CoT samples given each individual medical report. Experimental results and
various ablation studies show that our few-shot learning method achieves
satisfactory performance compared with fine-tuning strategies and gains superb
advantages when the available data is limited. The code and sample dataset of
the proposed CohortGPT model is available at:
https://anonymous.4open.science/r/CohortGPT-4872/Comment: 16 pages, 10 figure
Reliability analysis of all components in structural systems based on adaptive point estimate method and the principle of maximum entropy
Date: May 14 (Mon), 2018Place: ROHM Plaza Meeting Room, Kyoto University Katsura Campus, Kyoto, JAPANSupported by JSPS-NSFC Japan-China Scientific Cooperation ProjectOrganized by Structural Engineering of Buildings Laboratory, Department of Architecture and Architectural Engineering, Kyoto Universit
Toll-like receptor 9 interaction with CpG ODN – An in silico analysis approach
BACKGROUND: Toll-like receptor 9 (TLR9) recognises unmethylated CpG DNA and activates a signalling cascade, leading to the production of inflammatory cytokines such as TNF-α, IL-1, IL-6 and IL-12 via the adaptor protein MyD88. However, the specific sequence and structural requirements of the CpG DNA for the recognition of and binding to TLR9 are unknown. Moreover, the 3D structures of TLR9 and the TLR9-ODN complex have not been determined. In this study, we propose a reliable model of the interaction of the TLR9 ECD with CpG ODN using bioinformatics tools. RESULTS: The three-dimensional structures of two TLR9 ECD-CpG ODN complexes were constructed using a homology modelling and docking strategy. Based on the models of these complexes, the TLR9 ECD-CpG ODN interaction patterns were calculated. The results showed that the interface between the human TLR9 and the CpG ODN molecule is geometrically complementary. The computed molecular interactions indicated that LRR11 is the main region of TLR9 that binds to CpG ODN and that five positively charged residues within LRR11 are involved in the binding of the TLR9 ECD to the CpG ODN. Observations in the close-up view of these interactions indicated that these five positively charged residues contribute differently to the binding region within the TLR9 ECD-CpG ODN complex. 337Arg and 338Lys reside in the binding sites of ODN, forming hydrogen bonds and direct contacts with the CpG ODN, whereas 347Lys, 348Arg, and 353His do not directly contact the CpG ODN. These results are in agreement with previously reported experimental data. CONCLUSION: In this study, we present two structural models for the human and mouse TLR9 ECD in a complex with CpG ODN. Some features predicted by this model are consistent with previously reported experimental data. This complex model may lead to a better understanding of the function of TLR9 and its interaction with CpG ODN and will improve our understanding of TLR9-ligand interaction in general
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
Modern supervised learning neural network models require a large amount of
manually labeled data, which makes the construction of domain-specific
knowledge graphs time-consuming and labor-intensive. In parallel, although
there has been much research on named entity recognition and relation
extraction based on distantly supervised learning, constructing a
domain-specific knowledge graph from large collections of textual data without
manual annotations is still an urgent problem to be solved. In response, we
propose an integrated framework for adapting and re-learning knowledge graphs
from one coarse domain (biomedical) to a finer-define domain (oncology). In
this framework, we apply distant-supervision on cross-domain knowledge graph
adaptation. Consequently, no manual data annotation is required to train the
model. We introduce a novel iterative training strategy to facilitate the
discovery of domain-specific named entities and triples. Experimental results
indicate that the proposed framework can perform domain adaptation and
construction of knowledge graph efficiently
Reliability assessment of deteriorating structures using Bayesian updated probability density evolution method (PDEM)
Date: May 14 (Mon), 2018Place: ROHM Plaza Meeting Room, Kyoto University Katsura Campus, Kyoto, JAPANSupported by JSPS-NSFC Japan-China Scientific Cooperation ProjectOrganized by Structural Engineering of Buildings Laboratory, Department of Architecture and Architectural Engineering, Kyoto Universit
MediViSTA-SAM: Zero-shot Medical Video Analysis with Spatio-temporal SAM Adaptation
In recent years, the Segmentation Anything Model (SAM) has attracted
considerable attention as a foundational model well-known for its robust
generalization capabilities across various downstream tasks. However, SAM does
not exhibit satisfactory performance in the realm of medical image analysis. In
this study, we introduce the first study on adapting SAM on video segmentation,
called MediViSTA-SAM, a novel approach designed for medical video segmentation.
Given video data, MediViSTA, spatio-temporal adapter captures long and short
range temporal attention with cross-frame attention mechanism effectively
constraining it to consider the immediately preceding video frame as a
reference, while also considering spatial information effectively.
Additionally, it incorporates multi-scale fusion by employing a U-shaped
encoder and a modified mask decoder to handle objects of varying sizes. To
evaluate our approach, extensive experiments were conducted using
state-of-the-art (SOTA) methods, assessing its generalization abilities on
multi-vendor in-house echocardiography datasets. The results highlight the
accuracy and effectiveness of our network in medical video segmentation
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
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