[HTML][HTML] A gentle introduction to deep learning in medical image processing
This paper tries to give a gentle introduction to deep learning in medical image processing,
proceeding from theoretical foundations to applications. We first discuss general reasons for …
proceeding from theoretical foundations to applications. We first discuss general reasons for …
A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches
Microorganisms play a vital role in human life. Therefore, microorganism detection is of great
significance to human beings. However, the traditional manual microscopic detection …
significance to human beings. However, the traditional manual microscopic detection …
Computerized calculation of mitotic count distribution in canine cutaneous mast cell tumor sections: mitotic count is area dependent
CA Bertram, M Aubreville, C Gurtner… - Veterinary …, 2020 - journals.sagepub.com
Mitotic count (MC) is an important element for grading canine cutaneous mast cell tumors
(ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic …
(ccMCTs) and is determined in 10 consecutive high-power fields with the highest mitotic …
Digital microscopy, image analysis, and virtual slide repository
Advancements in technology and digitization have ushered in novel ways of enhancing
tissue-based research via digital microscopy and image analysis. Whole slide imaging …
tissue-based research via digital microscopy and image analysis. Whole slide imaging …
Automated semantic segmentation of red blood cells for sickle cell disease
Red blood cell (RBC) segmentation and classification from microscopic images is a crucial
step for the diagnosis of sickle cell disease (SCD). In this work, we adopt a deep learning …
step for the diagnosis of sickle cell disease (SCD). In this work, we adopt a deep learning …
Precision learning: towards use of known operators in neural networks
In this paper, we consider the use of prior knowledge within neural networks. In particular,
we investigate the effect of a known transform within the mapping from input data space to …
we investigate the effect of a known transform within the mapping from input data space to …
Deep learning in biomedical informatics
CL Hung - Intelligent Nanotechnology, 2023 - Elsevier
With the massive influx of multimodal data in the last decade, the role of data analytics in
health informatics has grown rapidly. Deep learning (DL) is defined as a technology based …
health informatics has grown rapidly. Deep learning (DL) is defined as a technology based …
Deep learning for medical image recognition: open issues and a way to forward
In the recent decade, deep learning has taken lead over available analysis techniques.
Today's deep learning is used in diversified sectors like health care, traffic management …
Today's deep learning is used in diversified sectors like health care, traffic management …
[HTML][HTML] Tcnn: A transformer convolutional neural network for artifact classification in whole slide images
The production of pathological slides is a complex task requiring several physical and
chemical procedures that are often done manually. Occasionally, such procedures may end …
chemical procedures that are often done manually. Occasionally, such procedures may end …
Magnifying networks for images with billions of pixels
N Dimitriou, O Arandjelovic - arXiv preprint arXiv:2112.06121, 2021 - arxiv.org
The shift towards end-to-end deep learning has brought unprecedented advances in many
areas of computer vision. However, deep neural networks are trained on images with …
areas of computer vision. However, deep neural networks are trained on images with …