This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain an...This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.展开更多
The successful outcome of any minimally invasive procedure is highly dependent on the imaging chain, as the medical team has to rely on indirect visualization of the surgical field during the entire procedure. During ...The successful outcome of any minimally invasive procedure is highly dependent on the imaging chain, as the medical team has to rely on indirect visualization of the surgical field during the entire procedure. During the last decade, the quality of the images obtainable pre- and intraoperatively has evolved significantly. In addition to the introduction of intra-operative image acquisition techniques such as ultrasound, X-ray, CT or MR, optical imaging technology as well as the corresponding processing units have undergone a rapid development. The article will review the activity related to minimally invasive procedures at the Operating Rooms of the Future (FOR) at St. Olavs Hospital, University Hospital of Trondheim, Norway. The imaging related demands of several surgical fields are introduced and the evolution of the imaging and visualization techniques at FOR will be presented. Subsequently, ongoing research projects in a dedicated visualization laboratory will be discussed and the advantages of updating the imaging equipment continuously in order to keep up with the latest developments in the field will be presented. It will be shown that the quality of the image acquisition and display can be significantly improved when compared to today’s standard. In addition to increasing the surgeon’s confidence, better imaging will lead to increased patient safety as well as more efficient interventions.展开更多
Medical visual question answering(Med-VQA)is a task that aims to answer clinical questions given a medical image.Existing literature generally treats it as a classic classification task based on interaction features o...Medical visual question answering(Med-VQA)is a task that aims to answer clinical questions given a medical image.Existing literature generally treats it as a classic classification task based on interaction features of the image and question.However,such a paradigm ignores the valuable semantics of candidate answers as well as their relations.From the real-world dataset,we observe that:1)The text of candidate answers has a strong intrinsic correlation with medical images;2)Subtle differences among multiple candidate answers are crucial for identifying the correct one.Therefore,we propose an answer semantics enhanced(ASE)method to integrate the semantics of answers and capture their subtle differences.Specifically,we enhance the semantic correlation of image-question-answer triplets by aligning images and question-answer tuples within the feature fusion module.Then,we devise a contrastive learning loss to highlight the semantic differences between the correct answer and other answers.Finally,extensive experiments demonstrate the effectiveness of our method.展开更多
文摘This study presents an innovative approach to enhancing the security of visual medical data in the generative AI environment through the integration of blockchain technology.By combining the strengths of blockchain and generative AI,the research team aimed to address the timely challenge of safeguarding visual medical content.The participating researchers conducted a comprehensive analysis,examining the vulnerabilities of medical AI services,personal information protection issues,and overall security weaknesses.This multi faceted exploration led to an indepth evaluation of the model’s performance and security.Notably,the correlation between accuracy,detection rate,and error rate was scrutinized.This analysis revealed insights into the model’s strengths and limitations,while the consideration of standard deviation shed light on the model’s stability and performance variability.The study proposed practical improvements,emphasizing the reduction of false negatives to enhance detection rate and leveraging blockchain technology to ensure visual data integrity in medical applications.Applying blockchain to generative AI-created medical content addresses key personal information protection issues.By utilizing the distributed ledger system of blockchain,the research team aimed to protect the privacy and integrity of medical data especially medical images.This approach not only enhances security but also enables transparent and tamperproof record-keeping.Additionally,the use of generative AI models ensures the creation of novel medical content without compromising personal information,further safeguarding patient privacy.In conclusion,this study showcases the potential of blockchain-based solutions in the medical field,particularly in securing sensitive medical data and protecting patient privacy.The proposed approach,combining blockchain and generative AI,offers a promising direction toward more robust and secure medical content management.Further research and advancements in this area will undoubtedly contribute to the development of robust and privacy-preserving healthcare systems,and visual diagnostic systems.
文摘The successful outcome of any minimally invasive procedure is highly dependent on the imaging chain, as the medical team has to rely on indirect visualization of the surgical field during the entire procedure. During the last decade, the quality of the images obtainable pre- and intraoperatively has evolved significantly. In addition to the introduction of intra-operative image acquisition techniques such as ultrasound, X-ray, CT or MR, optical imaging technology as well as the corresponding processing units have undergone a rapid development. The article will review the activity related to minimally invasive procedures at the Operating Rooms of the Future (FOR) at St. Olavs Hospital, University Hospital of Trondheim, Norway. The imaging related demands of several surgical fields are introduced and the evolution of the imaging and visualization techniques at FOR will be presented. Subsequently, ongoing research projects in a dedicated visualization laboratory will be discussed and the advantages of updating the imaging equipment continuously in order to keep up with the latest developments in the field will be presented. It will be shown that the quality of the image acquisition and display can be significantly improved when compared to today’s standard. In addition to increasing the surgeon’s confidence, better imaging will lead to increased patient safety as well as more efficient interventions.
基金supported by National Natural Science Foundation of China(Nos.62032013 and 62102074)the Science and Technology Projects in Liaoning Province,China(No.2023JH3/10200005).
文摘Medical visual question answering(Med-VQA)is a task that aims to answer clinical questions given a medical image.Existing literature generally treats it as a classic classification task based on interaction features of the image and question.However,such a paradigm ignores the valuable semantics of candidate answers as well as their relations.From the real-world dataset,we observe that:1)The text of candidate answers has a strong intrinsic correlation with medical images;2)Subtle differences among multiple candidate answers are crucial for identifying the correct one.Therefore,we propose an answer semantics enhanced(ASE)method to integrate the semantics of answers and capture their subtle differences.Specifically,we enhance the semantic correlation of image-question-answer triplets by aligning images and question-answer tuples within the feature fusion module.Then,we devise a contrastive learning loss to highlight the semantic differences between the correct answer and other answers.Finally,extensive experiments demonstrate the effectiveness of our method.
