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Found 58 papers, 7 papers with code
Extracting 2D weak labels from volume labels using multiple instance learning in CT hemorrhage detection
Multiple instance learning (MIL) is a supervised learning methodology that aims to allow models to learn instance class labels from bag class labels, where a bag is defined to contain multiple instances.
Safe deep reinforcement learning-based constrained optimal control scheme for active distribution networks
In this scheme, the optimal voltage control problem is formulated as a constrained Markov decision process, in which both state and action spaces are continuous.
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning
However, for data-intensive models such as vision transformer (ViT), current fine-tuning based FSL approaches are inefficient in knowledge generalization and thus degenerate the downstream task performances.
Eye-gaze-guided Vision Transformer for Rectifying Shortcut Learning
To address this problem, we propose to infuse human experts' intelligence and domain knowledge into the training of deep neural networks.
Coupling Visual Semantics of Artificial Neural Networks and Human Brain Function via Synchronized Activations
However, whether there exists semantic correlations/connections between the visual representations in ANNs and those in BNNs remains largely unexplored due to both the lack of an effective tool to link and couple two different domains, and the lack of a general and effective framework of representing the visual semantics in BNNs such as human functional brain networks (FBNs).
Is Multi-Task Learning an Upper Bound for Continual Learning?
Moreover, it is imaginable that when learning from multiple tasks, a small subset of these tasks could behave as adversarial tasks reducing the overall learning performance in a multi-task setting.
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
Disentangled Representation Learning
Disentangled Representation Learning (DRL) aims to learn a model capable of identifying and disentangling the underlying factors hidden in the observable data in representation form.
Gyri vs. Sulci: Disentangling Brain Core-Periphery Functional Networks via Twin-Transformer
To solve this fundamental problem, we design a novel Twin-Transformer framework to unveil the unique functional roles of gyri and sulci as well as their relationship in the whole brain function.
Mask-guided BERT for Few Shot Text Classification
The main challenge of FSL is the difficulty of training robust models on small amounts of samples, which frequently leads to overfitting.
AugGPT: Leveraging ChatGPT for Text Data Augmentation
Text data augmentation is an effective strategy for overcoming the challenge of limited sample sizes in many natural language processing (NLP) tasks.
DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4
The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy.
Coupling Artificial Neurons in BERT and Biological Neurons in the Human Brain
Linking computational natural language processing (NLP) models and neural responses to language in the human brain on the one hand facilitates the effort towards disentangling the neural representations underpinning language perception, on the other hand provides neurolinguistics evidence to evaluate and improve NLP models.
CP-CNN: Core-Periphery Principle Guided Convolutional Neural Network
In this study, We explore a novel brain-inspired design principle based on the core-periphery property of the human brain network to guide the design of CNNs.
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.
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.
Summary of ChatGPT-Related Research and Perspective Towards the Future of Large Language Models
This paper presents a comprehensive survey of ChatGPT-related (GPT-3. 5 and GPT-4) research, state-of-the-art large language models (LLM) from the GPT series, and their prospective applications across diverse domains.
ImpressionGPT: An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on the 'Findings' section.
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
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.
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
The proposed method uses the strengths of LLMs' understanding and logical reasoning to correct the incomplete logical facts for optimizing the performance of perceptual module, by summarizing and reorganizing reasoning rules represented in natural language format.
Differentiate ChatGPT-generated and Human-written Medical Texts
We focus on analyzing the differences between medical texts written by human experts and generated by ChatGPT, and designing machine learning workflows to effectively detect and differentiate medical texts generated by ChatGPT.
Prompt Engineering for Healthcare: Methodologies and Applications
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aiming to enhance their performance on specific tasks.
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downstream tasks.
SAM for Poultry Science
This study aims to assess the zero-shot segmentation performance of SAM on representative chicken segmentation tasks, including part-based segmentation and the use of infrared thermal images, and to explore chicken-tracking tasks by using SAM as a segmentation tool.
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.
Radiology-GPT: A Large Language Model for Radiology
We introduce Radiology-GPT, a large language model for radiology.
AD-AutoGPT: An Autonomous GPT for Alzheimer's Disease Infodemiology
In this pioneering study, inspired by AutoGPT, the state-of-the-art open-source application based on the GPT-4 large language model, we develop a novel tool called AD-AutoGPT which can conduct data collection, processing, and analysis about complex health narratives of Alzheimer's Disease in an autonomous manner via users' textual prompts.
Segment Anything Model (SAM) for Radiation Oncology
Given that SAM, a model pre-trained purely on natural images, can handle the delineation of OARs from medical images with clinically acceptable accuracy, these results highlight SAM's robust generalization capabilities with consistent accuracy in automatic segmentation for radiotherapy.
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications
ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships.
SAMAug: Point Prompt Augmentation for Segment Anything Model
Starting with an initial point prompt, SAM produces an initial mask, which is then fed into our proposed SAMAug to generate augmented point prompts.
Review of Large Vision Models and Visual Prompt Engineering
This review aims to summarize the methods employed in the computer vision domain for large vision models and visual prompt engineering, exploring the latest advancements in visual prompt engineering.
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease.
Hierarchical Semantic Tree Concept Whitening for Interpretable Image Classification
With the popularity of deep neural networks (DNNs), model interpretability is becoming a critical concern.
PharmacyGPT: The AI Pharmacist
In this study, we introduce PharmacyGPT, a novel framework to assess the capabilities of large language models (LLMs) such as ChatGPT and GPT-4 in emulating the role of clinical pharmacists.
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.
Evaluating Large Language Models for Radiology Natural Language Processing
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP).
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning.
Towards Artificial General Intelligence (AGI) in the Internet of Things (IoT): Opportunities and Challenges
Artificial General Intelligence (AGI), possessing the capacity to comprehend, learn, and execute tasks with human cognitive abilities, engenders significant anticipation and intrigue across scientific, commercial, and societal arenas.
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models
In this study, we investigate a privacy policy text analysis framework PolicyGPT based on the LLM.
ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data
Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels.
DT/MARS-CycleGAN: Improved Object Detection for MARS Phenotyping Robot
This work specifically tackles the first challenge by proposing a novel Digital-Twin(DT)MARS-CycleGAN model for image augmentation to improve our Modular Agricultural Robotic System (MARS)'s crop object detection from complex and variable backgrounds.
Transformation vs Tradition: Artificial General Intelligence (AGI) for Arts and Humanities
Recent advances in artificial general intelligence (AGI), particularly large language models and creative image generation systems have demonstrated impressive capabilities on diverse tasks spanning the arts and humanities.
Evaluating the Potential of Leading Large Language Models in Reasoning Biology Questions
Recent advances in Large Language Models (LLMs) have presented new opportunities for integrating Artificial General Intelligence (AGI) into biological research and education.
Evaluating multiple large language models in pediatric ophthalmology
IMPORTANCE The response effectiveness of different large language models (LLMs) and various individuals, including medical students, graduate students, and practicing physicians, in pediatric ophthalmology consultations, has not been clearly established yet.
Evaluating Large Language Models in Ophthalmology
Methods: A 100-item ophthalmology single-choice test was administered to three different LLMs (GPT-3. 5, GPT-4, and PaLM2) and three different professional levels (medical undergraduates, medical masters, and attending physicians), respectively.
Holistic Evaluation of GPT-4V for Biomedical Imaging
GPT-4V represents a breakthrough in artificial general intelligence (AGI) for computer vision, with applications in the biomedical domain.
Generative AI in Higher Education: Seeing ChatGPT Through Universities' Policies, Resources, and Guidelines
The advancements in Generative Artificial Intelligence (GenAI) technologies such as ChatGPT provide opportunities to enrich educational experiences, but also raise concerns about academic integrity if misused.
Ophtha-LLaMA2: A Large Language Model for Ophthalmology
In recent years, pre-trained large language models (LLMs) have achieved tremendous success in the field of Natural Language Processing (NLP).
Multimodality of AI for Education: Towards Artificial General Intelligence
This paper presents a comprehensive examination of how multimodal artificial intelligence (AI) approaches are paving the way towards the realization of Artificial General Intelligence (AGI) in educational contexts.
On the Promises and Challenges of Multimodal Foundation Models for Geographical, Environmental, Agricultural, and Urban Planning Applications
The advent of large language models (LLMs) has heightened interest in their potential for multimodal applications that integrate language and vision.
Understanding LLMs: A Comprehensive Overview from Training to Inference
Low-cost training and deployment of LLMs represent the future development trend.
Large Language Models for Robotics: Opportunities, Challenges, and Perspectives
Notably, in the realm of robot task planning, LLMs harness their advanced reasoning and language comprehension capabilities to formulate precise and efficient action plans based on natural language instructions.
Assessing Large Language Models in Mechanical Engineering Education: A Study on Mechanics-Focused Conceptual Understanding
This study is a pioneering endeavor to investigate the capabilities of Large Language Models (LLMs) in addressing conceptual questions within the domain of mechanical engineering with a focus on mechanics.
Revolutionizing Finance with LLMs: An Overview of Applications and Insights
Additionally, we conducted holistic tests on multiple financial tasks through the combination of natural language instructions.
LLMs for Coding and Robotics Education
To teach young children how to code and compete in robot challenges, large language models are being utilized for robot code explanation, generation, and modification.
Reasoning before Comparison: LLM-Enhanced Semantic Similarity Metrics for Domain Specialized Text Analysis
In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like ROUGE and BLEU.
Few-Shot Learning for Annotation-Efficient Nucleus Instance Segmentation
In this paper, we propose to formulate annotation-efficient nucleus instance segmentation from the perspective of few-shot learning (FSL).
Eye-gaze Guided Multi-modal Alignment Framework for Radiology
Additionally, we explore the impact of varying amounts of eye-gaze data on model performance, highlighting the feasibility and utility of integrating this auxiliary data into multi-modal pre-training.
