Results 1 to 10 of about 9,895,443 (300)
Segment Anything in Medical Images [PDF]
arXiv.org, 2023 Medical image segmentation is a critical component in clinical practice, facilitating accurate diagnosis, treatment planning, and disease monitoring. However, existing methods, often tailored to specific modalities or disease types, lack generalizability
Jun Ma, Bo Wang
semanticscholar +2 more sources
pixelNeRF: Neural Radiance Fields from One or Few Images [PDF]
Computer Vision and Pattern Recognition, 2020 We propose pixelNeRF, a learning framework that predicts a continuous neural scene representation conditioned on one or few input images. The existing approach for constructing neural radiance fields [27] involves optimizing the representation to every ...
Alex Yu +3 more
semanticscholar +1 more source
Data-efficient and weakly supervised computational pathology on whole-slide images [PDF]
Nature Biomedical Engineering, 2020 Deep-learning methods for computational pathology require either manual annotation of gigapixel whole-slide images (WSIs) or large datasets of WSIs with slide-level labels and typically suffer from poor domain adaptation and interpretability.
Ming Y. Lu +5 more
semanticscholar +1 more source
FaceForensics++: Learning to Detect Manipulated Facial Images [PDF]
IEEE International Conference on Computer Vision, 2019 The rapid progress in synthetic image generation and manipulation has now come to a point where it raises significant concerns for the implications towards society.
Andreas Rössler +5 more
semanticscholar +1 more source
Hypercomplex Image- to- Image Translation
2022 International Joint Conference on Neural Networks (IJCNN), 2022 Image-to-image translation (I2I) aims at transferring the content representation from an input domain to an output one, bouncing along different target domains. Recent I2I generative models, which gain outstanding results in this task, comprise a set of diverse deep networks each with tens of million parameters.
Eleonora Grassucci +3 more
openaire +3 more sources
Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks [PDF]
Physical and Engineering Sciences in Medicine, 2020 In this study, a dataset of X-ray images from patients with common bacterial pneumonia, confirmed Covid-19 disease, and normal incidents, was utilized for the automatic detection of the Coronavirus disease.
Ioannis D. Apostolopoulos +1 more
semanticscholar +1 more source
SyncDreamer: Generating Multiview-consistent Images from a Single-view Image [PDF]
International Conference on Learning Representations, 2023 In this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views
Yuan Liu +6 more
semanticscholar +1 more source
Blended Diffusion for Text-driven Editing of Natural Images [PDF]
Computer Vision and Pattern Recognition, 2021 Natural language offers a highly intuitive interface for image editing. In this paper, we introduce the first solution for performing local (region-based) edits in generic natural images, based on a natural language description along with an ROI mask. We
Omri Avrahami, D. Lischinski, Ohad Fried
semanticscholar +1 more source