scholarly journals MR‐based treatment planning in radiation therapy using a deep learning approach

2019 ◽  
Vol 20 (3) ◽  
pp. 105-114 ◽  
Author(s):  
Fang Liu ◽  
Poonam Yadav ◽  
Andrew M. Baschnagel ◽  
Alan B. McMillan
Keyword(s):  
Radiation Therapy ◽  
Deep Learning ◽  
Treatment Planning ◽  
Learning Approach

scholarly journals Semi-automatic sigmoid colon segmentation in CT for radiation therapy treatment planning via an iterative 2.5-D deep learning approach

2021 ◽  
Vol 68 ◽  
pp. 101896
Author(s):  
Yesenia Gonzalez ◽  
Chenyang Shen ◽  
Hyunuk Jung ◽  
Dan Nguyen ◽  
Steve B. Jiang ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Deep Learning ◽  
Treatment Planning ◽  
Sigmoid Colon ◽  
Learning Approach ◽  
Radiation Therapy Treatment Planning ◽  
Colon Segmentation ◽  
Therapy Treatment

Dosimetry-Driven Quality Measure of Brain Pseudo Computed Tomography Generated From Deep Learning for MRI-Only Radiation Therapy Treatment Planning

2020 ◽  
Vol 108 (3) ◽  
pp. 813-823
Author(s):  
Emilie Alvarez Andres ◽  
Lucas Fidon ◽  
Maria Vakalopoulou ◽  
Marvin Lerousseau ◽  
Alexandre Carré ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Treatment Planning ◽  
Quality Measure ◽  
Radiation Therapy Treatment Planning ◽  
Therapy Treatment

scholarly journals Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices

2020 ◽  
Vol 153 ◽  
pp. 228-235
Author(s):  
Roya Norouzi Kandalan ◽  
Dan Nguyen ◽  
Nima Hassan Rezaeian ◽  
Ana M. Barragán-Montero ◽  
Sebastiaan Breedveld ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Treatment Planning ◽  
Model Adaptation ◽  
Dose Prediction ◽  
Therapy Model

Technical Note: Deep Learning approach for automatic detection and identification of patient positioning devices for radiation therapy

2020 ◽  
Vol 47 (10) ◽  
pp. 5061-5069
Author(s):  
David H. Thomas ◽  
Leah K. Schubert ◽  
Yevgeniy Vinogradskiy ◽  
Sameer Nath ◽  
Brian Kavanagh ◽  
...  
Keyword(s):  
Radiation Therapy ◽  
Deep Learning ◽  
Patient Positioning ◽  
Automatic Detection ◽  
Technical Note ◽  
Learning Approach ◽  
Detection And Identification ◽  
Positioning Devices

scholarly journals A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity‐modulated radiation therapy

2020 ◽  
Vol 47 (3) ◽  
pp. 880-897 ◽  
Author(s):  
Azar Sadeghnejad Barkousaraie ◽  
Olalekan Ogunmolu ◽  
Steve Jiang ◽  
Dan Nguyen
Keyword(s):  
Prostate Cancer ◽  
Radiation Therapy ◽  
Deep Learning ◽  
Intensity Modulated Radiation Therapy ◽  
Learning Approach ◽  
Intensity Modulated ◽  
Beam Orientation Optimization ◽  
Beam Orientation ◽  
Orientation Optimization

Comparison of various Activation Functions A Deep Learning Approach

2018 ◽  
Vol 6 (3) ◽  
pp. 122-126
Author(s):  
Mohammed Ibrahim Khan ◽  
◽  
Akansha Singh ◽  
Anand Handa ◽  
◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Activation Functions

A Deep Learning based Arabic Script Recognition System: Benchmark on KHAT

2020 ◽  
Vol 17 (3) ◽  
pp. 299-305 ◽  
Author(s):  
Riaz Ahmad ◽  
Saeeda Naz ◽  
Muhammad Afzal ◽  
Sheikh Rashid ◽  
Marcus Liwicki ◽  
...  
Keyword(s):  
Deep Learning ◽  
Character Recognition ◽  
Data Augmentation ◽  
Short Term Memory ◽  
Recognition System ◽  
Learning Approach ◽  
Arabic Text ◽  
Data Set ◽  
Processing Step ◽  
Handwritten Arabic

This paper presents a deep learning benchmark on a complex dataset known as KFUPM Handwritten Arabic TexT (KHATT). The KHATT data-set consists of complex patterns of handwritten Arabic text-lines. This paper contributes mainly in three aspects i.e., (1) pre-processing, (2) deep learning based approach, and (3) data-augmentation. The pre-processing step includes pruning of white extra spaces plus de-skewing the skewed text-lines. We deploy a deep learning approach based on Multi-Dimensional Long Short-Term Memory (MDLSTM) networks and Connectionist Temporal Classification (CTC). The MDLSTM has the advantage of scanning the Arabic text-lines in all directions (horizontal and vertical) to cover dots, diacritics, strokes and fine inflammation. The data-augmentation with a deep learning approach proves to achieve better and promising improvement in results by gaining 80.02% Character Recognition (CR) over 75.08% as baseline.

Author response for "Deep learning and reinforcement learning approach on microgrid"

2020 ◽  
Author(s):  
Kumar Chandrasekaran ◽  
Prabaakaran Kandasamy ◽  
Srividhya Ramanathan
Keyword(s):  
Deep Learning ◽  
Reinforcement Learning ◽  
Author Response ◽  
Learning Approach

Deep Learning Approach for the Detection of Depression in Twitter

2019 ◽  
Author(s):  
Aswathy K S ◽  
Rafeeque P C ◽  
Reena Murali
Keyword(s):  
Deep Learning ◽  
Learning Approach ◽  
Detection Of Depression
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