A review of deep learning-based multiple-lesion recognition from medical images: classification, detection and segmentation
H Jiang, Z Diao, T Shi, Y Zhou, F Wang, W Hu… - Computers in Biology …, 2023 - Elsevier
Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …
processing with the advancement of deep learning in natural image classification, detection …
Hyperparameter optimization: Foundations, algorithms, best practices, and open challenges
Most machine learning algorithms are configured by a set of hyperparameters whose values
must be carefully chosen and which often considerably impact performance. To avoid a time …
must be carefully chosen and which often considerably impact performance. To avoid a time …
Medmnist v2-a large-scale lightweight benchmark for 2d and 3d biomedical image classification
We introduce MedMNIST v2, a large-scale MNIST-like dataset collection of standardized
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …
biomedical images, including 12 datasets for 2D and 6 datasets for 3D. All images are pre …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
MedViT: a robust vision transformer for generalized medical image classification
Abstract Convolutional Neural Networks (CNNs) have advanced existing medical systems
for automatic disease diagnosis. However, there are still concerns about the reliability of …
for automatic disease diagnosis. However, there are still concerns about the reliability of …
Challenges in deploying machine learning: a survey of case studies
In recent years, machine learning has transitioned from a field of academic research interest
to a field capable of solving real-world business problems. However, the deployment of …
to a field capable of solving real-world business problems. However, the deployment of …
[PDF][PDF] H2o automl: Scalable automatic machine learning
E LeDell, S Poirier - Proceedings of the AutoML Workshop at ICML, 2020 - automl.org
H2O is an open source, distributed machine learning platform designed to scale to very
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …
large datasets, with APIs in R, Python, Java and Scala. We present H2O AutoML, a highly …
Dos and don'ts of machine learning in computer security
With the growing processing power of computing systems and the increasing availability of
massive datasets, machine learning algorithms have led to major breakthroughs in many …
massive datasets, machine learning algorithms have led to major breakthroughs in many …
Hyper-parameter optimization: A review of algorithms and applications
T Yu, H Zhu - arxiv preprint arxiv:2003.05689, 2020 - arxiv.org
Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …
everyday lives. Machine learning provides more rational advice than humans are capable of …
[HTML][HTML] Automated machine learning: Review of the state-of-the-art and opportunities for healthcare
J Waring, C Lindvall, R Umeton - Artificial intelligence in medicine, 2020 - Elsevier
Objective This work aims to provide a review of the existing literature in the field of
automated machine learning (AutoML) to help healthcare professionals better utilize …
automated machine learning (AutoML) to help healthcare professionals better utilize …