Results 31 to 40 of about 9,677,595 (330)
Transfer learning for medical image classification: a literature review
BMC Medical Imaging, 2022 Background Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance.
Hee E. Kim +5 more
semanticscholar +1 more source
Classifications and imaging of juvenile spondyloarthritis
Journal of Ultrasonography, 2018 Juvenile spondyloarthritis may be present in at least 3 subtypes of juvenile idiopathic arthritis according to the classification of the International League of Associations for Rheumatology. By contrast with spondyloarthritis in adults, juvenile spondyloarthritis starts with inflammation of peripheral joints and entheses in the majority of children ...
Sudoł-Szopińska, Iwona +5 more
openaire +5 more sources
Identity Documents Classification as an Image Classification Problem [PDF]
, 2017 This paper studies the classification of images of identification documents. This problem is critical in various security context where proposed system must offer high performances. We address this challenge as an image classification problem, which has received a large attention from the scientific community.
Sicre, Ronan +2 more
openaire +3 more sources
Tensor-Based Algorithms for Image Classification [PDF]
, 2019 Interest in machine learning with tensor networks has been growing rapidly in recent years. We show that tensor-based methods developed for learning the governing equations of dynamical systems from data can, in the same way, be used for supervised ...
Gelß, Patrick, Klus, Stefan
core +2 more sources
TransMed: Transformers Advance Multi-Modal Medical Image Classification [PDF]
Diagnostics, 2021 Over the past decade, convolutional neural networks (CNN) have shown very competitive performance in medical image analysis tasks, such as disease classification, tumor segmentation, and lesion detection.
Yin Dai, Yifan Gao
semanticscholar +1 more source
Classification of user image descriptions [PDF]
International Journal of Human-Computer Studies, 2004 In order to resolve the mismatch between user needs and current image retrieval techniques, we conducted a study to get more information about what users look for in images. First, we developed a framework for the classification of image descriptions by users, based on various classification methods from the literature.
Hollink, L. +3 more
openaire +4 more sources
Pyramidal RoR for image classification [PDF]
Cluster Computing, 2017 The Residual Networks of Residual Networks (RoR) exhibits excellent performance in the image classification task, but sharply increasing the number of feature map channels makes the characteristic information transmission incoherent, which losses a certain of information related to classification prediction, limiting the classification performance.
Zhenbing Zhao +3 more
openaire +2 more sources
Soybean image dataset for classification
Data in Brief, 2023 This paper presents a dataset with 5513 images of individual soybean seeds, which encompass five categories: (Ⅰ) Intact, (Ⅱ) Immature, (Ⅲ) Skin-damaged, (Ⅳ) Spotted, and (Ⅴ) Broken. Furthermore, there are over 1000 images of soybean seeds in each category. Those images of individual soybeans were classified into five categories based on the Standard of
Wei Lin +8 more
openaire +3 more sources
Transformation Pursuit for Image Classification [PDF]
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014 A simple approach to learning invariances in image clas- sification consists in augmenting the training set with transformed versions of the original images. However, given a large set of possible transformations, selecting a com- pact subset is challenging.
Mattis Paulin +4 more
openaire +3 more sources
Class-Incremental Learning: Survey and Performance Evaluation on Image Classification [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020 For future learning systems, incremental learning is desirable because it allows for: efficient resource usage by eliminating the need to retrain from scratch at the arrival of new data; reduced memory usage by preventing or limiting the amount of data ...
Marc Masana +5 more
semanticscholar +1 more source