Machine learning methods for small data challenges in molecular science
B Dou, Z Zhu, E Merkurjev, L Ke, L Chen… - Chemical …, 2023 - ACS Publications
Small data are often used in scientific and engineering research due to the presence of
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
various constraints, such as time, cost, ethics, privacy, security, and technical limitations in …
[HTML][HTML] Federated learning for secure IoMT-applications in smart healthcare systems: A comprehensive review
Recent developments in the Internet of Things (IoT) and various communication
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
technologies have reshaped numerous application areas. Nowadays, IoT is assimilated into …
Deep learning models-based CT-scan image classification for automated screening of COVID-19
COVID-19 is the most transmissible disease, caused by the SARS-CoV-2 virus that severely
infects the lungs and the upper respiratory tract of the human body. This virus badly affected …
infects the lungs and the upper respiratory tract of the human body. This virus badly affected …
Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model …
Purpose To recognize and address various sources of bias essential for algorithmic fairness
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
and trustworthiness and to contribute to a just and equitable deployment of AI in medical …
Masked image modeling advances 3d medical image analysis
Recently, masked image modeling (MIM) has gained considerable attention due to its
capacity to learn from vast amounts of unlabeled data and has been demonstrated to be …
capacity to learn from vast amounts of unlabeled data and has been demonstrated to be …
Swinmm: masked multi-view with swin transformers for 3d medical image segmentation
Recent advancements in large-scale Vision Transformers have made significant strides in
improving pre-trained models for medical image segmentation. However, these methods …
improving pre-trained models for medical image segmentation. However, these methods …
A smart healthcare framework for detection and monitoring of COVID-19 using IoT and cloud computing
Abstract Coronavirus (COVID-19) is a very contagious infection that has drawn the world's
attention. Modeling such diseases can be extremely valuable in predicting their effects …
attention. Modeling such diseases can be extremely valuable in predicting their effects …
Self-adaptive moth flame optimizer combined with crossover operator and Fibonacci search strategy for COVID-19 CT image segmentation
The COVID-19 is one of the most significant obstacles that humanity is now facing. The use
of computed tomography (CT) images is one method that can be utilized to recognize …
of computed tomography (CT) images is one method that can be utilized to recognize …
Robust and interpretable medical image classifiers via concept bottleneck models
Medical image classification is a critical problem for healthcare, with the potential to alleviate
the workload of doctors and facilitate diagnoses of patients. However, two challenges arise …
the workload of doctors and facilitate diagnoses of patients. However, two challenges arise …
[HTML][HTML] COVID-19 detection and analysis from lung CT images using novel channel boosted CNNs
In December 2019, the global pandemic COVID-19 in Wuhan, China, affected human life
and the worldwide economy. Therefore, an efficient diagnostic system is required to control …
and the worldwide economy. Therefore, an efficient diagnostic system is required to control …