The reliable detection of diseases using deep learning-based medical image fusion (DLMIF) is a common practice nowadays. The performance of DLMIF depends on the features chosen for the fusion weight calculation. In this study, we examine the efficacy of convolutional neural network (CNN) features...
M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin et al., “Interactive medical image segmentation using deep learning with image-specific fine tuning,” IEEE Trans. Med. Imag, vol. 37, no. 7, pp. 1562–1573, 2018. ...
Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. A comprehensive thematic survey on medical image segmentation using deep learning techniques is presented. This paper makes two origin...
Razzak MI, Alhaqbani B (2015) Automatic detection of malarial parasite using microscopic blood images. J Med Imaging Health Inform 5(3):591–598 ArticleGoogle Scholar Shirazi SH, Umar AI, Haq NU, Naz S, Razzak MI (2015) Accurate micro-scopic red blood cell image enhancement and segmentati...
Automatic medical image segmentation plays a critical role in scientific research and medical care. Existing high-performance deep learning methods typically rely on large training datasets with high-quality manual annotations, which are difficult to obt
"deep reinforcement learning", "anatomical landmark detection" 2017 Low Dose CT Image Denoising Using a Generative Adversarial Network with Wasserstein Distance and Perceptual Loss [paper] Automatic Liver Segmentation Using an Adversarial Image-to-Image Network MICCAI 2017 [paper] Sharpness-aware Low Dos...
An Improved Pix2Pix GAN forMedical Image Generation 来自 Springer 喜欢 0 阅读量: 6 作者:Y Deng,J Ling,X Rao,J Tan,X Fu,S Li 摘要: Generating missing modality medical images using deep learning is crucial for disease diagnosis, treatment planning, and medical education. With the rapid ...
Deep learning-based medical image segmentation has made great progress over the past decades. Scholars have proposed many novel transformer-based segmentation networks to solve the problems of building long-range dependencies and global context connectio
Wang Z, Chen J, Hoi S (2020) Deep Learning for Image super-resolution: A Survey. IEEE Trans Pattern Anal Mach Intell 43(10):3365–3387.https://doi.org/10.1109/TPAMI.2020.2982166 Google Scholar Chen Z, Pawar K, Ekanayake M et al (2022) Deep learning for image enhancement and correction...
Section 3 describes the contributions of deep learning to canonical tasks in medical image analysis: classification, detection, segmentation, registration, retrieval, image generation and enhancement. Section 4 discusses obtained results and open challenges in different application areas: neuro, ...