HybridGAD: Identification of AI-Generated Radiology Abstracts Based on a Novel Hybrid Model with Attention Mechanism

Class Title:Radiological imaging method a comprehensive overview purpose.This GPT paper provides an overview of the different forms of radiological imaging and the potential diagnosis capabilities they offer as well as recent advances in the field.Materials and Methods:This paper provides an overview of conventional radiography digital radiography panoramic radiography computed tomography and cone-beam computed tomography.Additionally recent advances in radiological imaging are discussed such as imaging diagnosis and modern computer-aided diagnosis systems.Results:This paper details the differences between the imaging techniques the benefits of each and the current advances in the field to aid in the diagnosis of medical conditions.Conclusion:Radiological imaging is an extremely important tool in modern medicine to assist in medical diagnosis.This work provides an overview of the types of imaging techniques used the recent advances made and their potential applications.

ChatGPT, AI-generated content, and engineering management

This study explores the integration of ChatGPT and AI-generated content (AIGC) in engineering management. It assesses the impact of AIGC services on engineering management processes, mapping out the potential development of AIGC in various engineering functions. The study categorizes AIGC services within the domain of engineering management and conceptualizes an AIGC-aided engineering lifecycle. It also identifies key challenges and emerging trends associated with AIGC. The challenges highlighted are ethical considerations, reliability, and robustness in engineering management. The emerging trends are centered on AIGC-aided optimization design, AIGC-aided engineering consulting, and AIGC-aided green engineering initiatives.

A review of design intelligence:progress,problems,and challenges

Design intelligence is an important branch of artificial intelligence(AI),focusing on the intelligent models and algorithms in creativity and design.In the context of AI 2.0,studies on design intelligence have developed rapidly.We summarize mainly the current emerging framework of design intelligence and review the state-of-the-art techniques of related topics,including user needs analysis,ideation,content generation,and design evaluation.Specifically,the models and methods of intelligence-generated content are reviewed in detail.Finally,we discuss some open problems and challenges for future research in design intelligence.

CamDiff:Camouflage Image Augmentation via Diffusion

The burgeoning field of Camouflaged Object Detection(COD)seeks to identify objects that blend into their surroundings.Despite the impressive performance of recent learning-based models,their robustness is limited,as existing methods may misclassify salient objects as camouflaged ones,despite these contradictory characteristics.This limitation may stem from the lack of multipattern training images,leading to reduced robustness against salient objects.To overcome the scarcity of multi-pattern training images,we introduce CamDiff,a novel approach inspired by AI-Generated Content(AIGC).Specifically,we leverage a latent diffusion model to synthesize salient objects in camouflaged scenes,while using the zero-shot image classification ability of the Contrastive Language-Image Pre-training(CLIP)model to prevent synthesis failures and ensure that the synthesized objects align with the input prompt.Consequently,the synthesized image retains its original camouflage label while incorporating salient objects,yielding camouflaged scenes with richer characteristics.The results of user studies show that the salient objects in our synthesized scenes attract the user’s attention more;thus,such samples pose a greater challenge to the existing COD models.Our CamDiff enables flexible editing and effcient large-scale dataset generation at a low cost.It significantly enhances the training and testing phases of COD baselines,granting them robustness across diverse domains.Our newly generated datasets and source code are available at https://github.com/drlxj/CamDiff.