Review
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Medical Multimodal Foundation Models in Clinical Diagnosis and Treatment: Applications, Challenges, and Future Directions
2024-12-03 17:50:19Kai Sun, Siyan Xue, Fuchun Sun, Haoran Sun, Yu Luo, Ling Wang, Siyuan Wang, Na Guo, Lei Liu, Tian Zhao, Xinzhou Wang, Lei Yang, Shuo Jin, Jun Yan, Jiahong DongAbstract
Recent advancements in deep learning have significantly revolutionized the field of clinical diagnosis and treatment, offering novel approaches to improve diagnostic precision and treatment efficacy across diverse clinical domains, thus driving the pursuit of precision medicine. The growing availability of multi-organ and multimodal datasets has accelerated the development of large-scale Medical Multimodal Foundation Models (MMFMs). These models, known for their strong generalization capabilities and rich representational power, are increasingly being adapted to address a wide range of clinical tasks, from early diagnosis to personalized treatment strategies. This review offers a comprehensive analysis of recent developments in MMFMs, focusing on three key aspects: datasets, model architectures, and clinical applications. We also explore the challenges and opportunities in optimizing multimodal representations and discuss how these advancements are shaping the future of healthcare by enabling improved patient outcomes and more efficient clinical workflows.
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URL
https://arxiv.org/abs/2412.02621
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Remote Sensing Temporal Vision-Language Models: A Comprehensive Survey
2024-12-03 16:56:10Chenyang Liu, Jiafan Zhang, Keyan Chen, Man Wang, Zhengxia Zou, Zhenwei ShiAbstract
Temporal image analysis in remote sensing has traditionally centered on change detection, which identifies regions of change between images captured at different times. However, change detection remains limited by its focus on visual-level interpretation, often lacking contextual or descriptive information. The rise of Vision-Language Models (VLMs) has introduced a new dimension to remote sensing temporal image analysis by integrating visual information with natural language, creating an avenue for advanced interpretation of temporal image changes. Remote Sensing Temporal VLMs (RSTVLMs) allow for dynamic interactions, generating descriptive captions, answering questions, and providing a richer semantic understanding of temporal images. This temporal vision-language capability is particularly valuable for complex remote sensing applications, where higher-level insights are crucial. This paper comprehensively reviews the progress of RSTVLM research, with a focus on the latest VLM applications for temporal image analysis. We categorize and discuss core methodologies, datasets, and metrics, highlight recent advances in temporal vision-language tasks, and outline key challenges and future directions for research in this emerging field. This survey fills a critical gap in the literature by providing an integrated overview of RSTVLM, offering a foundation for further advancements in remote sensing temporal image understanding. We will keep tracing related works at \url{this https URL}
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https://arxiv.org/abs/2412.02573
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VISTA: A Panoramic View of Neural Representations
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We present VISTA (Visualization of Internal States and Their Associations), a novel pipeline for visually exploring and interpreting neural network representations. VISTA addresses the challenge of analyzing vast multidimensional spaces in modern machine learning models by mapping representations into a semantic 2D space. The resulting collages visually reveal patterns and relationships within internal representations. We demonstrate VISTA's utility by applying it to sparse autoencoder latents uncovering new properties and interpretations. We review the VISTA methodology, present findings from our case study ( this https URL ), and discuss implications for neural network interpretability across various domains of machine learning.
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https://arxiv.org/abs/2412.02412
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Selective Reviews of Bandit Problems in AI via a Statistical View
2024-12-03 08:28:47Pengjie Zhou, Haoyu Wei, Huiming ZhangAbstract
Reinforcement Learning (RL) is a widely researched area in artificial intelligence that focuses on teaching agents decision-making through interactions with their environment. A key subset includes stochastic multi-armed bandit (MAB) and continuum-armed bandit (SCAB) problems, which model sequential decision-making under uncertainty. This review outlines the foundational models and assumptions of bandit problems, explores non-asymptotic theoretical tools like concentration inequalities and minimax regret bounds, and compares frequentist and Bayesian algorithms for managing exploration-exploitation trade-offs. We also extend the discussion to $K$-armed contextual bandits and SCAB, examining their methodologies, regret analyses, and discussing the relation between the SCAB problems and the functional data analysis. Finally, we highlight recent advances and ongoing challenges in the field.
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https://arxiv.org/abs/2412.02251
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U-Net in Medical Image Segmentation: A Review of Its Applications Across Modalities
2024-12-03 08:11:06Fnu Neha, Deepshikha Bhati, Deepak Kumar Shukla, Sonavi Makarand Dalvi, Nikolaos Mantzou, Safa ShubbarAbstract
Medical imaging is essential in healthcare to provide key insights into patient anatomy and pathology, aiding in diagnosis and treatment. Non-invasive techniques such as X-ray, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Ultrasound (US), capture detailed images of organs, tissues, and abnormalities. Effective analysis of these images requires precise segmentation to delineate regions of interest (ROI), such as organs or lesions. Traditional segmentation methods, relying on manual feature-extraction, are labor-intensive and vary across experts. Recent advancements in Artificial Intelligence (AI) and Deep Learning (DL), particularly convolutional models such as U-Net and its variants (U-Net++ and U-Net 3+), have transformed medical image segmentation (MIS) by automating the process and enhancing accuracy. These models enable efficient, precise pixel-wise classification across various imaging modalities, overcoming the limitations of manual segmentation. This review explores various medical imaging techniques, examines the U-Net architectures and their adaptations, and discusses their application across different modalities. It also identifies common challenges in MIS and proposes potential solutions.
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https://arxiv.org/abs/2412.02242
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Leveraging Large Language Models for Comparative Literature Summarization with Reflective Incremental Mechanisms
2024-12-03 04:09:36Fernando Gabriela Garcia, Spencer Burns, Harrison FullerAbstract
In this paper, we introduce ChatCite, a novel method leveraging large language models (LLMs) for generating comparative literature summaries. The ability to summarize research papers with a focus on key comparisons between studies is an essential task in academic research. Existing summarization models, while effective at generating concise summaries, fail to provide deep comparative insights. ChatCite addresses this limitation by incorporating a multi-step reasoning mechanism that extracts critical elements from papers, incrementally builds a comparative summary, and refines the output through a reflective memory process. We evaluate ChatCite on a custom dataset, CompLit-LongContext, consisting of 1000 research papers with annotated comparative summaries. Experimental results show that ChatCite outperforms several baseline methods, including GPT-4, BART, T5, and CoT, across various automatic evaluation metrics such as ROUGE and the newly proposed G-Score. Human evaluation further confirms that ChatCite generates more coherent, insightful, and fluent summaries compared to these baseline models. Our method provides a significant advancement in automatic literature review generation, offering researchers a powerful tool for efficiently comparing and synthesizing scientific research.
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https://arxiv.org/abs/2412.02149
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Trust & Safety of LLMs and LLMs in Trust & Safety
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In recent years, Large Language Models (LLMs) have garnered considerable attention for their remarkable abilities in natural language processing tasks. However, their widespread adoption has raised concerns pertaining to trust and safety. This systematic review investigates the current research landscape on trust and safety in LLMs, with a particular focus on the novel application of LLMs within the field of Trust and Safety itself. We delve into the complexities of utilizing LLMs in domains where maintaining trust and safety is paramount, offering a consolidated perspective on this emerging trend.\ By synthesizing findings from various studies, we identify key challenges and potential solutions, aiming to benefit researchers and practitioners seeking to understand the nuanced interplay between LLMs and Trust and Safety. This review provides insights on best practices for using LLMs in Trust and Safety, and explores emerging risks such as prompt injection and jailbreak attacks. Ultimately, this study contributes to a deeper understanding of how LLMs can be effectively and responsibly utilized to enhance trust and safety in the digital realm.
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https://arxiv.org/abs/2412.02113
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The Evolution and Future Perspectives of Artificial Intelligence Generated Content
2024-12-02 20:16:40Chengzhang Zhu, Luobin Cui, Ying Tang, Jiacun WangAbstract
Artificial intelligence generated content (AIGC), a rapidly advancing technology, is transforming content creation across domains, such as text, images, audio, and video. Its growing potential has attracted more and more researchers and investors to explore and expand its possibilities. This review traces AIGC's evolution through four developmental milestones-ranging from early rule-based systems to modern transfer learning models-within a unified framework that highlights how each milestone contributes uniquely to content generation. In particular, the paper employs a common example across all milestones to illustrate the capabilities and limitations of methods within each phase, providing a consistent evaluation of AIGC methodologies and their development. Furthermore, this paper addresses critical challenges associated with AIGC and proposes actionable strategies to mitigate them. This study aims to guide researchers and practitioners in selecting and optimizing AIGC models to enhance the quality and efficiency of content creation across diverse domains.
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https://arxiv.org/abs/2412.01948
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Human-Machine Interfaces for Subsea Telerobotics: From Soda-straw to Natural Language Interactions
2024-12-02 17:48:51Adnan Abdullah, Ruo Chen, David Blow, Thanakon Uthai, Eric Jing Du, Md Jahidul IslamAbstract
This review explores the evolution of human-machine interfaces (HMIs) for subsea telerobotics, tracing back the transition from traditional first-person "soda-straw" consoles (narrow field-of-view camera feed) to advanced interfaces powered by gesture recognition, virtual reality, and natural language models. First, we discuss various forms of subsea telerobotics applications, current state-of-the-art (SOTA) interface systems, and the challenges they face in robust underwater sensing, real-time estimation, and low-latency communication. Through this analysis, we highlight how advanced HMIs facilitate intuitive interactions between human operators and robots to overcome these challenges. A detailed review then categorizes and evaluates the cutting-edge HMI systems based on their offered features from both human perspectives (e.g., enhancing operator control and situational awareness) and machine perspectives (e.g., improving safety, mission accuracy, and task efficiency). Moreover, we examine the literature on bidirectional interaction and intelligent collaboration in terms of sensory feedback and intuitive control mechanisms for both physical and virtual interfaces. The paper concludes by identifying critical challenges, open research questions, and future directions, emphasizing the need for multidisciplinary collaboration in subsea telerobotics.
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URL
https://arxiv.org/abs/2412.01753
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Are We There Yet? Revealing the Risks of Utilizing Large Language Models in Scholarly Peer Review
2024-12-02 16:55:03Rui Ye, Xianghe Pang, Jingyi Chai, Jiaao Chen, Zhenfei Yin, Zhen Xiang, Xiaowen Dong, Jing Shao, Siheng ChenAbstract
Scholarly peer review is a cornerstone of scientific advancement, but the system is under strain due to increasing manuscript submissions and the labor-intensive nature of the process. Recent advancements in large language models (LLMs) have led to their integration into peer review, with promising results such as substantial overlaps between LLM- and human-generated reviews. However, the unchecked adoption of LLMs poses significant risks to the integrity of the peer review system. In this study, we comprehensively analyze the vulnerabilities of LLM-generated reviews by focusing on manipulation and inherent flaws. Our experiments show that injecting covert deliberate content into manuscripts allows authors to explicitly manipulate LLM reviews, leading to inflated ratings and reduced alignment with human reviews. In a simulation, we find that manipulating 5% of the reviews could potentially cause 12% of the papers to lose their position in the top 30% rankings. Implicit manipulation, where authors strategically highlight minor limitations in their papers, further demonstrates LLMs' susceptibility compared to human reviewers, with a 4.5 times higher consistency with disclosed limitations. Additionally, LLMs exhibit inherent flaws, such as potentially assigning higher ratings to incomplete papers compared to full papers and favoring well-known authors in single-blind review process. These findings highlight the risks of over-reliance on LLMs in peer review, underscoring that we are not yet ready for widespread adoption and emphasizing the need for robust safeguards.
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URL
https://arxiv.org/abs/2412.01708
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A comprehensive review of datasets and deep learning techniques for vision in Unmanned Surface Vehicles
2024-12-02 12:54:18Linh Trinh, Siegfried Mercelis, Ali AnwarAbstract
Unmanned Surface Vehicles (USVs) have emerged as a major platform in maritime operations, capable of supporting a wide range of applications. USVs can help reduce labor costs, increase safety, save energy, and allow for difficult unmanned tasks in harsh maritime environments. With the rapid development of USVs, many vision tasks such as detection and segmentation become increasingly important. Datasets play an important role in encouraging and improving the research and development of reliable vision algorithms for USVs. In this regard, a large number of recent studies have focused on the release of vision datasets for USVs. Along with the development of datasets, a variety of deep learning techniques have also been studied, with a focus on USVs. However, there is a lack of a systematic review of recent studies in both datasets and vision techniques to provide a comprehensive picture of the current development of vision on USVs, including limitations and trends. In this study, we provide a comprehensive review of both USV datasets and deep learning techniques for vision tasks. Our review was conducted using a large number of vision datasets from USVs. We elaborate several challenges and potential opportunities for research and development in USV vision based on a thorough analysis of current datasets and deep learning techniques.
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URL
https://arxiv.org/abs/2412.01461
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Artificial Intelligence for Geometry-Based Feature Extraction, Analysis and Synthesis in Artistic Images: A Survey
2024-12-02 12:41:15Mridula Vijendran, Jingjing Deng, Shuang Chen, Edmond S. L. Ho, Hubert P. H. ShumAbstract
Artificial Intelligence significantly enhances the visual art industry by analyzing, identifying and generating digitized artistic images. This review highlights the substantial benefits of integrating geometric data into AI models, addressing challenges such as high inter-class variations, domain gaps, and the separation of style from content by incorporating geometric information. Models not only improve AI-generated graphics synthesis quality, but also effectively distinguish between style and content, utilizing inherent model biases and shared data traits. We explore methods like geometric data extraction from artistic images, the impact on human perception, and its use in discriminative tasks. The review also discusses the potential for improving data quality through innovative annotation techniques and the use of geometric data to enhance model adaptability and output refinement. Overall, incorporating geometric guidance boosts model performance in classification and synthesis tasks, providing crucial insights for future AI applications in the visual arts domain.
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https://arxiv.org/abs/2412.01450
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LMAct: A Benchmark for In-Context Imitation Learning with Long Multimodal Demonstrations
2024-12-02 12:31:58Anian Ruoss, Fabio Pardo, Harris Chan, Bonnie Li, Volodymyr Mnih, Tim GeneweinAbstract
Today's largest foundation models have increasingly general capabilities, yet when used as agents, they often struggle with simple reasoning and decision-making tasks, even though they possess good factual knowledge of the task and how to solve it. In this paper, we present a benchmark to pressure-test these models' multimodal decision-making capabilities in the very long-context regime (up to one million tokens) and investigate whether they can learn from a large number of expert demonstrations in their context. We evaluate a wide range of state-of-the-art frontier models as policies across a battery of simple interactive decision-making tasks: playing tic-tac-toe, chess, and Atari, navigating grid worlds, solving crosswords, and controlling a simulated cheetah. We measure the performance of Claude 3.5 Sonnet, Gemini 1.5 Flash, Gemini 1.5 Pro, GPT-4o, o1-mini, and o1-preview under increasing amounts of expert demonstrations in the context $\unicode{x2013}$ from no demonstrations up to 512 full episodes, pushing these models' multimodal long-context reasoning capabilities to their limits. Across our tasks, today's frontier models rarely manage to fully reach expert performance, showcasing the difficulty of our benchmark. Presenting more demonstrations often has little effect, but some models steadily improve with more demonstrations on a few tasks. We investigate the effect of encoding observations as text or images and the impact of chain-of-thought prompting. Overall, our results suggest that even today's most capable models often struggle to imitate desired behavior by generalizing purely from in-context demonstrations. To help quantify the impact of other approaches and future innovations aiming to tackle this problem, we open source our benchmark that covers the zero-, few-, and many-shot regimes in a unified evaluation.
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https://arxiv.org/abs/2412.01441
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A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systems
2024-12-02 11:06:34Pang Li, Shahrul Azman Mohd Noah, Hafiz Mohd SarimAbstract
This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effectively modeling complex, non-linear relationships within the data. We begin by exploring the fundamental principles of both collaborative filtering and deep neural networks, laying the groundwork for understanding their integration. Subsequently, we review key advancements in the field, categorizing various deep learning models that enhance CF systems, including Multilayer Perceptrons (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Graph Neural Networks (GNN), autoencoders, Generative Adversarial Networks (GAN), and Restricted Boltzmann Machines (RBM). The paper also discusses evaluation protocols, various publicly available auxiliary information, and data features. Furthermore, the survey concludes with a discussion of the challenges and future research opportunities in enhancing collaborative filtering systems with deep learning.
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https://arxiv.org/abs/2412.01378
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An overview of diffusion models for generative artificial intelligence
2024-12-02 10:55:38Davide Gallon, Arnulf Jentzen, Philippe von WurstembergerAbstract
This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main ideas behind training and generation procedures. In this overview article we also review selected extensions and improvements of the basic framework from the literature such as improved DDPMs, denoising diffusion implicit models, classifier-free diffusion guidance models, and latent diffusion models.
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https://arxiv.org/abs/2412.01371
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Best Practices for Large Language Models in Radiology
2024-12-02 07:54:55Christian Bluethgen, Dave Van Veen, Cyril Zakka, Katherine Link, Aaron Fanous, Roxana Daneshjou, Thomas Frauenfelder, Curtis Langlotz, Sergios Gatidis, Akshay ChaudhariAbstract
At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, and concisely documenting and communicating the outcomes. The emergence of large language models (LLMs) offers an opportunity to improve the management and interpretation of the vast data in radiology. Despite being primarily general-purpose, these advanced computational models demonstrate impressive capabilities in specialized language-related tasks, even without specific training. Unlocking the potential of LLMs for radiology requires basic understanding of their foundations and a strategic approach to navigate their idiosyncrasies. This review, drawing from practical radiology and machine learning expertise and recent literature, provides readers insight into the potential of LLMs in radiology. It examines best practices that have so far stood the test of time in the rapidly evolving landscape of LLMs. This includes practical advice for optimizing LLM characteristics for radiology practices along with limitations, effective prompting, and fine-tuning strategies.
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https://arxiv.org/abs/2412.01233
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Object Tracking in a $360^o$ View: A Novel Perspective on Bridging the Gap to Biomedical Advancements
2024-12-02 04:43:50Mojtaba S. Fazli, Shannon QuinnAbstract
Object tracking is a fundamental tool in modern innovation, with applications in defense systems, autonomous vehicles, and biomedical research. It enables precise identification, monitoring, and spatiotemporal analysis of objects across sequential frames, providing insights into dynamic behaviors. In cell biology, object tracking is vital for uncovering cellular mechanisms, such as migration, interactions, and responses to drugs or pathogens. These insights drive breakthroughs in understanding disease progression and therapeutic interventions. Over time, object tracking methods have evolved from traditional feature-based approaches to advanced machine learning and deep learning frameworks. While classical methods are reliable in controlled settings, they struggle in complex environments with occlusions, variable lighting, and high object density. Deep learning models address these challenges by delivering greater accuracy, adaptability, and robustness. This review categorizes object tracking techniques into traditional, statistical, feature-based, and machine learning paradigms, with a focus on biomedical applications. These methods are essential for tracking cells and subcellular structures, advancing our understanding of health and disease. Key performance metrics, including accuracy, efficiency, and adaptability, are discussed. The paper explores limitations of current methods and highlights emerging trends to guide the development of next-generation tracking systems for biomedical research and broader scientific domains.
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https://arxiv.org/abs/2412.01119
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Uhura: A Benchmark for Evaluating Scientific Question Answering and Truthfulness in Low-Resource African Languages
2024-12-01 19:46:40Edward Bayes, Israel Abebe Azime, Jesujoba O. Alabi, Jonas Kgomo, Tyna Eloundou, Elizabeth Proehl, Kai Chen, Imaan Khadir, Naome A. Etori, Shamsuddeen Hassan Muhammad, Choice Mpanza, Igneciah Pocia Thete, Dietrich Klakow, David Ifeoluwa AdelaniAbstract
Evaluations of Large Language Models (LLMs) on knowledge-intensive tasks and factual accuracy often focus on high-resource languages primarily because datasets for low-resource languages (LRLs) are scarce. In this paper, we present Uhura -- a new benchmark that focuses on two tasks in six typologically-diverse African languages, created via human translation of existing English benchmarks. The first dataset, Uhura-ARC-Easy, is composed of multiple-choice science questions. The second, Uhura-TruthfulQA, is a safety benchmark testing the truthfulness of models on topics including health, law, finance, and politics. We highlight the challenges creating benchmarks with highly technical content for LRLs and outline mitigation strategies. Our evaluation reveals a significant performance gap between proprietary models such as GPT-4o and o1-preview, and Claude models, and open-source models like Meta's LLaMA and Google's Gemma. Additionally, all models perform better in English than in African languages. These results indicate that LMs struggle with answering scientific questions and are more prone to generating false claims in low-resource African languages. Our findings underscore the necessity for continuous improvement of multilingual LM capabilities in LRL settings to ensure safe and reliable use in real-world contexts. We open-source the Uhura Benchmark and Uhura Platform to foster further research and development in NLP for LRLs.
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URL
https://arxiv.org/abs/2412.00948
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Opus: A Large Work Model for Complex Workflow Generation
2024-11-30 20:00:41Th\'eo Fagnoni, Bellinda Mesbah, Mahsun Altin, Phillip KingstonAbstract
This paper introduces Opus, a novel framework for generating and optimizing Workflows tailored to complex Business Process Outsourcing (BPO) use cases, focusing on cost reduction and quality enhancement while adhering to established industry processes and operational constraints. Our approach generates executable Workflows from Intention, defined as the alignment of Client Input, Client Output, and Process Context. These Workflows are represented as Directed Acyclic Graphs (DAGs), with nodes as Tasks consisting of sequences of executable Instructions, including tools and human expert reviews. We adopt a two-phase methodology: Workflow Generation and Workflow Optimization. In the Generation phase, Workflows are generated using a Large Work Model (LWM) informed by a Work Knowledge Graph (WKG) that encodes domain-specific procedural and operational knowledge. In the Optimization phase, Workflows are transformed into Workflow Graphs (WFGs), where optimal Workflows are determined through path optimization. Our experiments demonstrate that state-of-the-art Large Language Models (LLMs) face challenges in reliably retrieving detailed process data as well as generating industry-compliant workflows. The key contributions of this paper include: - The integration of a Work Knowledge Graph (WKG) into a Large Work Model (LWM), enabling the generation of context-aware, semantically aligned, structured and auditable Workflows. - A two-phase approach that combines Workflow Generation from Intention with graph-based Workflow Optimization. - Opus Alpha 1 Large and Opus Alpha 1 Small, models that outperform state-of-the-art LLMs by 38\% and 29\% respectively in Workflow Generation for a Medical Coding use case.
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https://arxiv.org/abs/2412.00573
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From Audio Deepfake Detection to AI-Generated Music Detection -- A Pathway and Overview
2024-11-30 19:53:23Yupei Li, Manuel Milling, Lucia Specia, Bj\"orn W. SchullerAbstract
As Artificial Intelligence (AI) technologies continue to evolve, their use in generating realistic, contextually appropriate content has expanded into various domains. Music, an art form and medium for entertainment, deeply rooted into human culture, is seeing an increased involvement of AI into its production. However, the unregulated use of AI music generation (AIGM) tools raises concerns about potential negative impacts on the music industry, copyright and artistic integrity, underscoring the importance of effective AIGM detection. This paper provides an overview of existing AIGM detection methods. To lay a foundation to the general workings and challenges of AIGM detection, we first review general principles of AIGM, including recent advancements in deepfake audios, as well as multimodal detection techniques. We further propose a potential pathway for leveraging foundation models from audio deepfake detection to AIGM detection. Additionally, we discuss implications of these tools and propose directions for future research to address ongoing challenges in the field.
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URL
https://arxiv.org/abs/2412.00571
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