8,870 research outputs found
From Nano to Macro: Overview of the IEEE Bio Image and Signal Processing Technical Committee
The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the
IEEE Signal Processing Society (SPS) promotes activities within the broad
technical field of biomedical image and signal processing. Areas of interest
include medical and biological imaging, digital pathology, molecular imaging,
microscopy, and associated computational imaging, image analysis, and
image-guided treatment, alongside physiological signal processing,
computational biology, and bioinformatics. BISP has 40 members and covers a
wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image
Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing,
BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: Bioinformatics. BISP
plays a central role in the organization of the IEEE International Symposium on
Biomedical Imaging (ISBI) and contributes to the technical sessions at the IEEE
International Conference on Acoustics, Speech and Signal Processing (ICASSP),
and the IEEE International Conference on Image Processing (ICIP). In this
paper, we provide a brief history of the TC, review the technological and
methodological contributions its community delivered, and highlight promising
new directions we anticipate
RCCNet: An Efficient Convolutional Neural Network for Histological Routine Colon Cancer Nuclei Classification
Efficient and precise classification of histological cell nuclei is of utmost
importance due to its potential applications in the field of medical image
analysis. It would facilitate the medical practitioners to better understand
and explore various factors for cancer treatment. The classification of
histological cell nuclei is a challenging task due to the cellular
heterogeneity. This paper proposes an efficient Convolutional Neural Network
(CNN) based architecture for classification of histological routine colon
cancer nuclei named as RCCNet. The main objective of this network is to keep
the CNN model as simple as possible. The proposed RCCNet model consists of only
1,512,868 learnable parameters which are significantly less compared to the
popular CNN models such as AlexNet, CIFARVGG, GoogLeNet, and WRN. The
experiments are conducted over publicly available routine colon cancer
histological dataset "CRCHistoPhenotypes". The results of the proposed RCCNet
model are compared with five state-of-the-art CNN models in terms of the
accuracy, weighted average F1 score and training time. The proposed method has
achieved a classification accuracy of 80.61% and 0.7887 weighted average F1
score. The proposed RCCNet is more efficient and generalized terms of the
training time and data over-fitting, respectively.Comment: Published in ICARCV 201
VCU Media Lab
We propose the establishment of a VCU Media Lab â a professional creative media technology unit whose mission is to support the development, design, production and delivery of innovative media, multimedia, computer-based instruction, publications and tools in support of VCU education, research and marketing initiatives. This centrally administered, budgeted and resourced facility will acknowledge, refine, focus and expand media services that are currently being provided at VCU in a decentralized manner
Better medicine through machine learning::Whatâs real, and whatâs artificial?
Machine Learning Special Issue Guest Editors Suchi Saria, Atul Butte, and Aziz Sheikh cut through the hyperbole with an accessible and accurate portrayal of the forefront of machine learning in clinical translation
Special issue on MICCAI 2018
status: publishe
Focal Spot, Spring 1987
https://digitalcommons.wustl.edu/focal_spot_archives/1045/thumbnail.jp
Advanced Computational Methods for Oncological Image Analysis.
The Special Issue "Advanced Computational Methods for Oncological Image Analysis", published for the Journal of Imaging, covered original research papers about state-of-the-art and novel algorithms and methodologies, as well as applications of computational methods for oncological image analysis, ranging from radiogenomics to deep learning [...]
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