If you look for the good, you will always find it!
IBSI Chapter 2_Image Filtering
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Developing SERA package and making it as a GUI application (Phase 1)
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HECKTOR challenge- 2022 Task 2: The prediction of patient outcomes, namely Relapse-Free Survival (RFS) from the FDG-PET/CT images and available clinical data.
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HECKTOR challenge- 2022 Task 1: The automatic segmentation of Head and Neck (H&N) primary tumors and lymph nodes (new!) in FDG-PET/CT images
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Breast Cancer Classification via Self-Knowledge Distillation, Transfer Learning, and Feature Fusion Models
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Hybrid Machine Learning Systems for Prediction of Parkinson’s Disease Pathogenic Variants using Clinical Information and Radiomics Feature
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Fusion-based head and neck tumor segmentation and survival prediction using robust deep learning techniques and advanced hybrid machine learning systems
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Advanced automatic segmentation of tumors and survival prediction in head and neck cancer
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Multi-Modality Fusion Coupled with Hybrid Machine Learning Systems for Improved Prediction of TNM Staging in Lung Cancer
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Investigating the relationship between whole-exome sequence analysis results of clinical features and features extracted from DaT SPECT images Description
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Identifying Reliable and Robust Tensor Radiomics Features in Lung Cancer
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Application of Deep Learning Techniques Coupled with fusion Models for prediction of TNM Stage in Lung Cancer
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Application of Hybrid Machine Learning Features and Reliable Tensor Radiomics features in prediction of Survival Outcome in Lung Cancer
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Automatic Segmentation of Head and Neck Cancer using Fusion, Attention map detection, and Deep leaning Techniques
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Predicting TNM Stage in Head and Neck Cancer using Multi-Modality Fusion Coupled with Deep Learning Techniques
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