Machine learning in medical applications: A review of state-of-the-art methods
Applications of machine learning (ML) methods have been used extensively to solve various
complex challenges in recent years in various application areas, such as medical, financial …
complex challenges in recent years in various application areas, such as medical, financial …
Deep learning techniques for medical image segmentation: achievements and challenges
Deep learning-based image segmentation is by now firmly established as a robust tool in
image segmentation. It has been widely used to separate homogeneous areas as the first …
image segmentation. It has been widely used to separate homogeneous areas as the first …
[HTML][HTML] The potential for artificial intelligence in healthcare
T Davenport, R Kalakota - Future healthcare journal, 2019 - Elsevier
The complexity and rise of data in healthcare means that artificial intelligence (AI) will
increasingly be applied within the field. Several types of AI are already being employed by …
increasingly be applied within the field. Several types of AI are already being employed by …
A survey on deep learning: Algorithms, techniques, and applications
The field of machine learning is witnessing its golden era as deep learning slowly becomes
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …
the leader in this domain. Deep learning uses multiple layers to represent the abstractions of …
Deep learning for healthcare: review, opportunities and challenges
Gaining knowledge and actionable insights from complex, high-dimensional and
heterogeneous biomedical data remains a key challenge in transforming health care …
heterogeneous biomedical data remains a key challenge in transforming health care …
Artificial intelligence, machine learning, deep learning, and cognitive computing: what do these terms mean and how will they impact health care?
SA Bini - The Journal of arthroplasty, 2018 - Elsevier
This article was presented at the 2017 annual meeting of the American Association of Hip
and Knee Surgeons to introduce the members gathered as the audience to the concepts …
and Knee Surgeons to introduce the members gathered as the audience to the concepts …
Applications of deep learning and reinforcement learning to biological data
Rapid advances in hardware-based technologies during the past decades have opened up
new possibilities for life scientists to gather multimodal data in various application domains …
new possibilities for life scientists to gather multimodal data in various application domains …
Fully dense UNet for 2-D sparse photoacoustic tomography artifact removal
Photoacoustic imaging is an emerging imaging modality that is based upon the
photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure …
photoacoustic effect. In photoacoustic tomography (PAT), the induced acoustic pressure …
Deep models under the GAN: information leakage from collaborative deep learning
Deep Learning has recently become hugely popular in machine learning for its ability to
solve end-to-end learning systems, in which the features and the classifiers are learned …
solve end-to-end learning systems, in which the features and the classifiers are learned …
Artificial intelligence in pharmaceutical and healthcare research
SK Bhattamisra, P Banerjee, P Gupta… - Big Data and Cognitive …, 2023 - mdpi.com
Artificial intelligence (AI) is a branch of computer science that allows machines to work
efficiently, can analyze complex data. The research focused on AI has increased …
efficiently, can analyze complex data. The research focused on AI has increased …