Athanasios Anagnostis
University of Thessaly
20 Papers
46 Citations
Athanasios Anagnostis is an academic researcher from University of Thessaly. The author has contributed to research in topics: Computer science & Bone scintigraphy. The author has an hindex of 7, co-authored 17 publications.
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Papers
Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.
Nikolaos Papandrianos,Elpiniki I. Papageorgiou,Athanasios Anagnostis,Konstantinos Papageorgiou +3 more
TL;DR: A CNN model with improved classification capabilities for bone metastasis diagnosis is produced, using bone scans from prostate cancer patients, and the proposed CNN-based approach outperforms the popular CNN methods in nuclear medicine for metastatic prostate cancer diagnosis in bones.
Human Activity Recognition through Recurrent Neural Networks for Human–Robot Interaction in Agriculture
Athanasios Anagnostis,Lefteris Benos,Dimitrios Tsaopoulos,Aristotelis C. Tagarakis,Naoum Tsolakis,Dionysis Bochtis +5 more
TL;DR: It can be inferred that the combination of all sensors can achieve the highest accuracy in human activity recognition, as concluded from a comparative analysis for each sensor’s impact on the model's performance.
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A Deep-Learning Approach for Diagnosis of Metastatic Breast Cancer in Bones from Whole-Body Scans
TL;DR: A robust CNN model is built that will be able to classify images of whole-body scans in patients suffering from breast cancer, depending on whether or not they are infected by metastasis of breast cancer.
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Efficient Bone Metastasis Diagnosis in Bone Scintigraphy Using a Fast Convolutional Neural Network Architecture.
Nikolaos Papandrianos,Elpiniki I. Papageorgiou,Athanasios Anagnostis,Konstantinos Papageorgiou +3 more
- 30 Jul 2020
TL;DR: The remarkable outcome of this study is the ability of the method for an easier and more precise interpretation of whole-body images, with effects on the diagnosis accuracy and decision making on the treatment to be applied.
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A deep learning approach for anthracnose infected trees classification in walnut orchards
Athanasios Anagnostis,Aristotelis C. Tagarakis,G. Asiminari,Elpiniki I. Papageorgiou,Dimitrios Kateris,Dimitrios Moshou,Dionysis Bochtis +6 more
TL;DR: In this paper, an object detector was trained to recognize anthracnose-infected walnut leaves and the trained model was applied to detect diseased trees in a 4-ha commercial walnut orchard.
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