Exploring Bioimage Synthesis and Detection via Generative Adversarial Networks: A Multi-Faceted Case Study [0.03%]
基于生成对抗网络的生物图像合成与检测：多方位案例研究
Valeria Sorgente,Dante Biagiucci,Mario Cesarelli et al.
Valeria Sorgente et al.
Background: Generative Adversarial Networks (GANs), thanks to their great versatility, have a plethora of applications in biomedical imaging with the goal of simulating complex pathological conditions and creating clinica...
Iterative Reconstruction with Dynamic ElasticNet Regularization for Nuclear Medicine Imaging [0.03%]
基于动态ElasticNet正则化的核医学成像迭代重建方法
Ryosuke Kasai,Hideki Otsuka
Ryosuke Kasai
This study proposes a novel image reconstruction algorithm for nuclear medicine imaging based on the maximum likelihood expectation maximization (MLEM) framework with dynamic ElasticNet regularization. Whereas conventional the L1 and L2 reg...
Underwater Image Enhancement Using a Diffusion Model with Adversarial Learning [0.03%]
基于对抗学习的扩散模型 underwater 图像增强方法
Xueyan Ding,Xiyu Chen,Yixin Sui et al.
Xueyan Ding et al.
Due to the distinctive attributes of underwater environments, underwater images frequently encounter challenges such as low contrast, color distortion, and noise. Current underwater image enhancement techniques often suffer from limited gen...
Yoshihisa Furushita,Daniele Baracchi,Marco Fontani et al.
Yoshihisa Furushita et al.
This study proposes a method to detect double compression in H.265/HEVC videos containing B-frames, a scenario underexplored in previous research. The method extracts frame-level encoding features-including frame type, coding unit (CU) size...
Transfer Learning Fusion Approaches for Colorectal Cancer Histopathological Image Analysis [0.03%]
结直肠癌组织病理图像分析的迁移学习融合方法研究
Houda Saif ALGhafri,Chia S Lim
Houda Saif ALGhafri
It is well-known that accurate classification of histopathological images is essential for effective diagnosis of colorectal cancer. Our study presents three attention-based decision fusion models that combine pre-trained CNNs (Inception V3...
The Robust Vessel Segmentation and Centerline Extraction: One-Stage Deep Learning Approach [0.03%]
一种基于深度学习的鲁棒的血管分割和中心线提取方法
Rostislav Epifanov,Yana Fedotova,Savely Dyachuk et al.
Rostislav Epifanov et al.
The accurate segmentation of blood vessels and centerline extraction are critical in vascular imaging applications, ranging from preoperative planning to hemodynamic modeling. This study introduces a novel one-stage method for simultaneous ...
Rüstem Özakar,Eyüp Gedikli
Rüstem Özakar
Hand hygiene is paramount for public health, especially in critical sectors like healthcare and the food industry. Ensuring compliance with recommended hand washing gestures is vital, necessitating autonomous evaluation systems leveraging m...
Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction [0.03%]
基于一類SVM和支持向量机脑部MRI肿瘤检测的特征提取优化方法
Azeddine Mjahad,Alfredo Rosado-Muñoz
Azeddine Mjahad
The early detection of brain tumors is critical for improving clinical outcomes and patient survival. However, medical imaging datasets frequently exhibit class imbalance, posing significant challenges for traditional classification algorit...
RGB-to-Infrared Translation Using Ensemble Learning Applied to Driving Scenarios [0.03%]
基于集成学习的RGB-To-红外驾驶场景图像转换技术
Leonardo Ravaglia,Roberto Longo,Kaili Wang et al.
Leonardo Ravaglia et al.
Multimodal sensing is essential in order to reach the robustness required of autonomous vehicle perception systems. Infrared (IR) imaging is of particular interest due to its low cost and complementarity with traditional RGB sensors. Howeve...
Hezha Mohammedkhan,Hein Fleuren,Çíçek Güven et al.
Hezha Mohammedkhan et al.
The prediction of anthropometric measurements from 2D body images, particularly for children, remains an under-explored area despite its potential applications in healthcare, fashion, and fitness. While pose estimation and body shape classi...