scholarly journals Enabling Autonomous Medical Image Data Annotation: A human-in-the-loop Reinforcement Learning Approach

2021 ◽  
Author(s):  
Leonardo da Cruz ◽  
César Sierra-Franco ◽  
Greis Silva-Calpa ◽  
Alberto Raposo
Keyword(s):  
Reinforcement Learning ◽  
Medical Image ◽  
Image Data ◽  
Learning Approach ◽  
Data Annotation ◽  
Human In The Loop ◽  
Medical Image Data

A neural network based deep learning approach for efficient segmentation of brain tumor medical image data

2019 ◽  
Vol 36 (5) ◽  
pp. 4227-4234 ◽  
Author(s):  
A. Veeramuthu ◽  
S. Meenakshi ◽  
K. Ashok Kumar
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Brain Tumor ◽  
Medical Image ◽  
Image Data ◽  
Learning Approach ◽  
Medical Image Data

Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods

2009 ◽  
Author(s):  
Heiko Seim ◽  
Dagmar Kainmueller ◽  
Markus Heller ◽  
Stefan Zachow ◽  
Hans-Christian Hege
Keyword(s):  
Medical Image ◽  
Image Data ◽  
Automatic Extraction ◽  
Anatomical Landmarks ◽  
Medical Image Data

Applying trajectory mining in medical image data

2017 ◽  
Author(s):  
Roland Ellerweg ◽  
Katharina Hofer ◽  
Artur Khromov ◽  
Peter Voigt ◽  
Sebastian Fischer
Keyword(s):  
Medical Image ◽  
Image Data ◽  
Trajectory Mining ◽  
Medical Image Data

scholarly journals Design and Implementation of Compressed Medical Data over an Authenticated Secured

2020 ◽  
Vol 9 (3) ◽  
pp. 1543-1548
Keyword(s):  
Medical Image ◽  
Research Work ◽  
Health Sector ◽  
Image Data ◽  
Medical Data ◽  
Information Network ◽  
Design And Implementation ◽  
Simulation Results ◽  
Medical Image Data ◽  
Secure Transmission

Ideally, secure transmission of medical image data is one of the major challenges in health sector. The National Health Information Network has to protect the data in confidential manner. Storage is also one of the basic concern along with secure transmission. In this paper we propose an algorithm that supports confidentiality, authentication and integrity implementation of the scrambled data before transmitting on the communication medium. Before communication the data is compressed while keeping data encrypted. The research work demonstrate with simulation results. The results shows that the proposed work effectively maintains confidentiality, authentication and integrity. The experimental results evaluated medical image quality like PSNR, MSE, SC, and NAEetc.

Similarity Searching of Medical Image Data in Distributed Systems

2012 ◽  
pp. 58-77
Author(s):  
Amalia Charisi ◽  
Panagiotis Korvesis ◽  
Vasileios Megalooikonomou
Keyword(s):  
Distributed Systems ◽  
Image Retrieval ◽  
Medical Image ◽  
Image Data ◽  
Medical Data ◽  
Similarity Searching ◽  
Network Bandwidth ◽  
Local Site ◽  
Distributed Index ◽  
Medical Image Data

In this paper, the authors propose a method for medical image retrieval in distributed systems to facilitate telemedicine. The proposed framework can be used by a network of healthcare centers, where some can be remotely located, assisting in diagnosis without the necessary transfer of patients. Security and confidentiality issues of medical data are expected, which are handled at the local site following the procedures and protocols of each institution. To make the search more effective, the authors introduce a distributed index based on features that are extracted from each image. Considering network bandwidth limitations and other restrictions that are associated with handling medical data, the images are processed locally and a pointer is distributed in the network. For the distribution of this pointer, the authors propose a function that maps the pointer of each image to a node with similar contents.

A user-guided tool for efficient segmentation of medical image data

1997 ◽  
pp. 685-694 ◽  
Author(s):  
Tuomo Vehkomäki ◽  
Guido Gerig ◽  
Gabor Székely
Keyword(s):  
Medical Image ◽  
Image Data ◽  
Medical Image Data
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