Machine learning methods for small data challenges in molecular science
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 …
A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches
With the development of Computer-aided Diagnosis (CAD) and image scanning techniques,
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
Whole-slide Image (WSI) scanners are widely used in the field of pathological diagnosis …
U-Net-STN: A novel end-to-end lake boundary prediction model
Detecting changes in land cover is a critical task in remote sensing image interpretation, with
particular significance placed on accurately determining the boundaries of lakes. Lake …
particular significance placed on accurately determining the boundaries of lakes. Lake …
The Role of generative adversarial network in medical image analysis: An in-depth survey
M AlAmir, M AlGhamdi - ACM Computing Surveys, 2022 - dl.acm.org
A generative adversarial network (GAN) is one of the most significant research directions in
the field of artificial intelligence, and its superior data generation capability has garnered …
the field of artificial intelligence, and its superior data generation capability has garnered …
Deep learning for medical image-based cancer diagnosis
Simple Summary Deep learning has succeeded greatly in medical image-based cancer
diagnosis. To help readers better understand the current research status and ideas, this …
diagnosis. To help readers better understand the current research status and ideas, this …
Hierarchical attention feature fusion-based network for land cover change detection with homogeneous and heterogeneous remote sensing images
Deep learning techniques have become popular in land cover change detection (LCCD)
with remote sensing images (RSIs). However, many existing networks mostly concentrate on …
with remote sensing images (RSIs). However, many existing networks mostly concentrate on …
Deep learning in electron microscopy
JM Ede - Machine Learning: Science and Technology, 2021 - iopscience.iop.org
Deep learning is transforming most areas of science and technology, including electron
microscopy. This review paper offers a practical perspective aimed at developers with …
microscopy. This review paper offers a practical perspective aimed at developers with …
Exploring Sentinel-1 and Sentinel-2 diversity for flood inundation map** using deep learning
Identification of flood water extent from satellite images has historically relied on either
synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to …
synthetic aperture radar (SAR) or multi-spectral (MS) imagery. MS sensors are limited to …
Segmentation-based classification deep learning model embedded with explainable AI for COVID-19 detection in chest X-ray scans
Background and Motivation: COVID-19 has resulted in a massive loss of life during the last
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
two years. The current imaging-based diagnostic methods for COVID-19 detection in …
Digital hair removal by deep learning for skin lesion segmentation
W Li, ANJ Raj, T Tjahjadi, Z Zhuang - Pattern Recognition, 2021 - Elsevier
Occlusion due to hair in dermoscopic images affects the diagnostic operation and the
accuracy of its analysis of a skin lesion. Also, dermis hair has the following different …
accuracy of its analysis of a skin lesion. Also, dermis hair has the following different …