Deep Learning (DL) methods are powerful analytical tools for microscopy and can outperform conventional image processing pipelines. Despite the enthusiasm and innovations fuelled by DL technology, the need to access powerful and compatible resources to train DL networks leads to an accessibility barrier ...
We also discuss the drawbacks of existing deep learning-based methods, especially on the challenges of training datasets acquisition and evaluation, and propose the potential solutions. Furthermore, the latest development of augmented intelligent microscopy that based on deep learning technology may lead ...
In this regard, deep learning (DL)-based image processing can be highly beneficial. The review summarises and critiques the use of DL in image processing for the data collected using various optical microscopic techniques. In tandem with optical microscopy, DL has already found applications in ...
Engagement is a key indicator of the quality of learning experience, and one that plays a major role in developing intelligent educational interfaces. Any such interface requires the ability to recognise the level of engagement in order to respond appropriately; however, there is very little existing...
intelligent prior-free image restoration solution for microscopists or even microscope companies, thus ensuring more precise biomedical applications for researchers. Introduction Fluorescence microscope is an indispensable tool for biomedical research, which can be used to obtain auxiliary information with ...
Deep learning based enhanced tumor segmentation approach for MR brain images Applied Soft Computing, 78 (10) (2019), pp. 346-354 View in ScopusGoogle Scholar 23. D. Abirami, N. Shalini, V. Rajinikanth, H. Lin, V. S. Rao Intelligent Data Engineering and Analytics Intelligent Data Engineer...
Deep learning (DL) is one of the fastest-growing topics in materials data science, with rapidly emerging applications spanning atomistic, image-based, spectral, and textual data modalities. DL allows analysis of unstructured data and automated identification of features. The recent development of large...
Deep learning has become a predominant method for solving data analysis problems in virtually all fields of science and engineering. The increasing complexity and the large volume of data collected by diverse sensor systems have spurred the development o
This advancement has further propelled the application of machine learning-based methods in the field of Earth sciences, particularly in the intelligent analysis and application of petrology [22]. It provides new perspectives and tools for Earth sciences. Machine learning-based lithology identification ...
Active learning, a framework addressing how to select training examples in order to train a model most efficiently, is shown to significantly reduce the time required by experts to annotate cell segmentation images in high-throughput high-context microscopy. Training deep learning models on this type...