Automatic Bone Metastasis Classification: An in-depth Comparison of CNN and Transformer Architectures-Reference-Cited by-同舟云学术

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Automatic Bone Metastasis Classification: An in-depth Comparison of CNN and Transformer Architectures

Published:2023-09-20 Issue: Volume: Page:
ISSN:
Container-title:2023 International Conference on Innovations in Intelligent Systems and Applications (INISTA)
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Author:

Afnouch Marwa¹,Gaddour Olfa²,Bougourzi Fares³,Hentati Yosr⁴,Ahmed Abdelmalik Taleb⁵,Abid Mohamed²

Affiliation:

1. ENIS, Sfax, Tunisia IEMN, UPHF,CES Lab,Valenciennes,France

2. University of Sfax,CES Laboratory, ENIS,Sfax,Tunisia

3. University Paris-Est Creteil,Laboratoire LISSI, Vitry Sur Seine,Paris,France

4. Hedi Chaker University Hospital,Department of Radiology,Sfax,Tunisia

5. UMR Univ. Polytechnique Hauts-de-France,Laboratory of IEMN, CNRS, Centrale Lille,Valenciennes,France

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10310305/10310314/10310593.pdf?arnumber=10310593

Reference31 articles.

1. & Others Deep Learning Model for Classifying Metastatic Epidural Spinal Cord Compression on MRI;hallinan;Frontiers in Oncology,2022

2. Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning

3. PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans

4. Radiomics-based machine learning models to distinguish between metastatic and healthy bone using lesion-center-based geometric regions of interest

5. A novel XGBoost extension for credit scoring class-imbalanced data combining a generalized extreme value link and a modified focal loss function

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