DEEP NEURAL
By Hojjat Salehinejad, Shahrokh Valaee, †. TimDowdell, †. ErrolColak, †. JosephBarfett
Subfields: Model Architectures
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Model Architectures
- Aykut Erdem
- Hojjat Salehinejad
- Erkut Erdem
- Shahrokh Valaee
- Derek F. Wong
- Lidia S. Chao
- Levent Karacan
- Phillip Isola
Citations in the corpus
- UM-pAligner: Neural Network-Based Parallel Sentence Identification Model (2018, background): “Gr´egoire and Langlais (2017) proposed using a recurrent neural network (RNN) for the parallel sentence identification task.”
- CheXclusion: Fairness gaps in deep chest X-ray classifiers (2020, background): “Deeplearning based medical image classifiers are one potential solution, with much work targeting chest X-rays specifically [25, 39, 45, 48], leveraging large-scale publicly available datasets [4, 11, 22, 45], and”
- Shifting machine learning for healthcare from development to deployment and from models to data (2022, unknown): “In healthcare, GANs have been used to augment datasets 18 , alleviate the problems of privacy-restricted 19 and unbalanced datasets 20 , and perform image-modality-to-image-modality translation 21 and image reconstruction 22 (Fig.”
- Deep Learning in Medical Imaging (2019, unknown): “56,57 GAN is a combination of 2 different neural networks that can generate realistic synthetic images.”
- Ensembling with Deep Generative Views (2021, background): “Specifically in the medical community, synthesizing additional images to augment limited-size datasets has been beneficial for classification [40, 60, 17, 22, 48] or segmentation tasks [32, 11, 20].”
- Recent Advances in Recurrent Neural Networks (2017, methodology): “The deep convolutional generative adversarial networks are developed in [124] to generate artificial chest radiographs for automated abnormality detection in chest radiographs.”
- Image Synthesis in Multi-Contrast MRI With Conditional Generative Adversarial Networks (2018, methodology): “liver lesion classification in synthetic CT [79], and chest pathology classification in synthetic X-ray images [80].”