Deep learning (DL) techniques have been extensively utilized for medical image classification. Most DL-based classification networks are generally structured hierarchically and optimized through the minimization of a single loss function measured at the end of the networks. However, such a single loss design could potentially lead to optimization of one specific value of interest but fail to leverage informative features from intermediate layers that might benefit classification performance and reduce the risk of overfitting. Recently, auxiliary convolutional neural networks (AuxCNNs) have been employed on top of traditional classification networks to facilitate the training of intermediate layers to improve classification performance and r...
With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learnin...
Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Tradition...
Artificial intelligence techniques involving the use of artificial neural networks (ie, deep learnin...
AI and Deep Learning have seen many exciting real-world applications implemented today. The applicat...
Recently, the healthcare industry is in a dynamic transformation accelerated by the availability of ...
Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis...
Data augmentation is widely used in image processing and pattern recognition problems in order to in...
Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growt...
In the recent years, deep learning has shown to have a formidable impact on image classification and...
Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patien...
While generative adversarial networks (GAN) have been widely applied in various settings, the compet...
Thesis (Master's)--University of Washington, 2018In recent years, machine learning techniques based ...
Due to the complexity of medical images, traditional medical image classification methods have been ...
Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of co...
Deep learning models are more often used in the medical field as a result of the rapid development o...
With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learnin...
Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Tradition...
Artificial intelligence techniques involving the use of artificial neural networks (ie, deep learnin...
AI and Deep Learning have seen many exciting real-world applications implemented today. The applicat...
Recently, the healthcare industry is in a dynamic transformation accelerated by the availability of ...
Segmentation of regions of interest (ROIs) in medical images is an important step for image analysis...
Data augmentation is widely used in image processing and pattern recognition problems in order to in...
Deep learning (DL) is one of the branches of artificial intelligence that has seen exponential growt...
In the recent years, deep learning has shown to have a formidable impact on image classification and...
Nowadays medical imaging plays a vital role in diagnosing the various types of diseases among patien...
While generative adversarial networks (GAN) have been widely applied in various settings, the compet...
Thesis (Master's)--University of Washington, 2018In recent years, machine learning techniques based ...
Due to the complexity of medical images, traditional medical image classification methods have been ...
Deep learning is nowadays at the forefront of artificial intelligence. More precisely, the use of co...
Deep learning models are more often used in the medical field as a result of the rapid development o...
With the development of Artificial Intelligence, the auxiliary diagnosis model based on deep learnin...
Medical image classification is a key technique of Computer-Aided Diagnosis (CAD) systems. Tradition...
Artificial intelligence techniques involving the use of artificial neural networks (ie, deep learnin...