It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in de...It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.展开更多
As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and s...As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.展开更多
Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflo...Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.展开更多
Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,p...Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.展开更多
基金Projects(41001260,61173122,61573380) supported by the National Natural Science Foundation of ChinaProject(11JJ5044) supported by the Hunan Provincial Natural Science Foundation of China
文摘It is illegal to spread and transmit pornographic images over internet,either in real or in artificial format.The traditional methods are designed to identify real pornographic images and they are less efficient in dealing with artificial images.Therefore,criminals turn to release artificial pornographic images in some specific scenes,e.g.,in social networks.To efficiently identify artificial pornographic images,a novel bag-of-visual-words based approach is proposed in the work.In the bag-of-words(Bo W)framework,speeded-up robust feature(SURF)is adopted for feature extraction at first,then a visual vocabulary is constructed through K-means clustering and images are represented by an improved Bo W encoding method,and finally the visual words are fed into a learning machine for training and classification.Different from the traditional BoW method,the proposed method sets a weight on each visual word according to the number of features that each cluster contains.Moreover,a non-binary encoding method and cross-matching strategy are utilized to improve the discriminative power of the visual words.Experimental results indicate that the proposed method outperforms the traditional method.
文摘As editors of Artificial Intelligence in Medical Imaging(AIMI),it is our great pleasure to take this opportunity to wish all of our authors,subscribers,readers,Editorial Board members,independent expert referees,and staff of the Editorial Office a Very Happy New Year.On behalf of the Editorial Team,we would like to express our gratitude to all of the authors who have contributed their valuable manuscripts,our independent referees,and our subscribers and readers for their continuous support,dedication,and encouragement.Together with an excellent of team effort by our Editorial Board members and staff of the Editorial Office,AIMI advanced in 2020 and we look forward to greater achievements in 2021.
文摘Large language models(LLMs)have emerged as transformative tools in radiology artificial intelligence(AI),offering significant capabilities in areas such as image report generation,clinical decision support,and workflow optimization.The first part of this manuscript presents a comprehensive overview of the current state of LLM applications in radiology,including their historical evolution,technical foundations,and practical uses.Despite notable advances,inherent architectural constraints,such as token-level sequential processing,limit their ability to perform deep abstract reasoning and holistic contextual understanding,which are critical for fine-grained diagnostic interpretation.We provide a critical perspective on current LLMs and discuss key challenges,including model reliability,bias,and explainability,highlighting the pressing need for novel approaches to advance radiology AI.Large concept models(LCMs)represent a nascent and promising paradigm in radiology AI,designed to transcend the limitations of token-level processing by utilizing higher-order conceptual representations and multimodal data integration.The second part of this manuscript introduces the foundational principles and theoretical framework of LCMs,highlighting their potential to facilitate enhanced semantic reasoning,long-range context synthesis,and improved clinical decision-making.Critically,the core of this section is the proposal of a novel theoretical framework for LCMs,formalized and extended from our group’s foundational concept-based models-the world’s earliest articulation of this paradigm for medical AI.This conceptual shift has since been externally validated and propelled by the recent publication of the LCM architectural proposal by Meta AI,providing a large-scale engineering blueprint for the future development of this technology.We also outline future research directions and the transformative implications of this emerging AI paradigm for radiologic practice,aiming to provide a blueprint for advancing toward human-like conceptual understanding in AI.While challenges persist,we are at the very beginning of a new era,and it is not unreasonable to hope that future advancements will overcome these hurdles,pushing the boundaries of AI in Radiology,far beyond even the most state-of-the-art models of today.
基金supported by the Researchers Supporting Program at King Saud University.Researchers Supporting Project number(RSPD2024R867),King Saud University,Riyadh,Saudi Arabia.
文摘Brain tumor is a global issue due to which several people suffer,and its early diagnosis can help in the treatment in a more efficient manner.Identifying different types of brain tumors,including gliomas,meningiomas,pituitary tumors,as well as confirming the absence of tumors,poses a significant challenge using MRI images.Current approaches predominantly rely on traditional machine learning and basic deep learning methods for image classification.These methods often rely on manual feature extraction and basic convolutional neural networks(CNNs).The limitations include inadequate accuracy,poor generalization of new data,and limited ability to manage the high variability in MRI images.Utilizing the EfficientNetB3 architecture,this study presents a groundbreaking approach in the computational engineering domain,enhancing MRI-based brain tumor classification.Our approach highlights a major advancement in employing sophisticated machine learning techniques within Computer Science and Engineering,showcasing a highly accurate framework with significant potential for healthcare technologies.The model achieves an outstanding 99%accuracy,exhibiting balanced precision,recall,and F1-scores across all tumor types,as detailed in the classification report.This successful implementation demonstrates the model’s potential as an essential tool for diagnosing and classifying brain tumors,marking a notable improvement over current methods.The integration of such advanced computational techniques in medical diagnostics can significantly enhance accuracy and efficiency,paving the way for wider application.This research highlights the revolutionary impact of deep learning technologies in improving diagnostic processes and patient outcomes in neuro-oncology.
