Think of many things; Do one.
ProjectID: P202208090001
Bronze NewMulti-Modality Fusion Coupled with Hybrid Machine Learning Systems for Improved Prediction of TNM Staging in …
Objective: Multi-level multi-modality-fusion-radiomics is a promising technique with the potential for improved prognostication of cancer. We aim to use advanced fusion techniques on PET and …
ProjectID: P202205180001
Bronze NewBreast Cancer Classification via Self-Knowledge Distillation, Transfer Learning, and Feature Fusion Models
Cancer is still considered as the most common source of human illness and death globally. Lately, the number of patients classified with cancer was 18.1 …
ProjectID: P202205130007
Bronze NewPredicting TNM Stage in Head and Neck Cancer using Multi-Modality Fusion Coupled with Deep Learning …
Objective: The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head and-neck-squamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist …
ProjectID: P202205130006
Bronze NewAutomatic Segmentation of Head and Neck Cancer using Fusion, Attention map detection, and Deep learning …
Objectives: Squamous cell carcinoma of the head and neck (HNSCC) is a malignant tumor of the head and neck that originates from the lips, mouth, …
ProjectID: P202205130005
Bronze NewApplication of Hybrid Machine Learning Features and Reliable Tensor Radiomics features in prediction of Survival …
Objectives: Radiomics is a major frontier in medical image analysis, enabling mining of high-dimensional data from images. Although radiomics features (RF) are increasingly extracted via …
ProjectID: P202205130004
Bronze NewApplication of Deep Learning Techniques Coupled with fusion Models for prediction of TNM Stage in …
Objective: The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of lung cancer in a specific manner to assist with assessment …
ProjectID: P202205130003
Bronze NewIdentifying Reliable and Robust Tensor Radiomics Features in Lung Cancer
Objectives: Radiomics is a major frontier in medical image analysis, enabling the mining of high-dimensional data from images. Although radiomics features (RF) are increasingly extracted …
ProjectID: P202208030002
Bronze OngoingBreast cancer diagnosis using an integrated radiomic framework and machine learning methods based on DCE-MRI …
Worldwide, breast cancer is the most common cancer in women, including almost one-third of all females' malignancies. Previous studies indicate that various genetic and environmental …
Aug. 10, 2022
ProjectID: P202208030001
Bronze OngoingFeasibility study of synthesis from CT to PET using CycleGAN network for head and neck …
The objective of image translation is to map an image onto another image with different domains. A growing number of applications in computer vision and …
Aug. 3, 2022
ProjectID: P202205230001
Silver OngoingHECKTOR challenge- 2022 Task 2: The prediction of patient outcomes, namely Relapse-Free Survival (RFS) from …
Following the success of the first two editions of the HECKTOR challenge in 2020 and 2021, this challenge will be presented at the 25th International …
May 23, 2022
ProjectID: P202205180002
Silver OngoingHECKTOR challenge- 2022 Task 1: The automatic segmentation of Head and Neck (H&N) primary tumors …
Following the success of the first two editions of the HECKTOR challenge in 2020 and 2021, this challenge will be presented at the 25th International …
May 18, 2022
ProjectID: P202205130002
Bronze OngoingInvestigating the relationship between whole-exome sequence analysis results of clinical features and features extracted from …
Parkinson’s disease (PD) is the second prevalent neurodegenerative disorder after Alzheimer’s disease (AD), is affected 1–5% of the general population and 1-2% of the world …
May 13, 2022
ProjectID: P202206060002
Gold DoneIBSI Chapter 2_medical Image Filtering (Reproducible medical imaging features)
Medical imaging is often used to support clinical decision-making, but only through visual inspection or simple measures. Additional relevant information, e.g., disease phenotypes, may be …
June 6, 2022
ProjectID: P202205230002
Gold DoneDeveloping SERA package and making it as a GUI application
Supporting, Reading, and Writing Medical Images Conversion Modalities Implementing Image Registration and Fusion Linking SERA with other sections
Oct. 23, 2021
ProjectID: P202205170005
Bronze DoneHybrid Machine Learning Systems for Prediction of Parkinson’s Disease Pathogenic Variants using Clinical Information and …
Objectives: Parkinson’s disease (PD) is a complex neurodegenerative disorder characterized by motor and non-motor symptoms. 5–10% of cases are of genetic origin with mutations identified …
May 17, 2022
ProjectID: P202205170004
Bronze DoneFusion-based head and neck tumor segmentation and survival prediction using robust deep learning techniques and …
Multi-level multi-modality fusion radiomics is a promising technique with potential for improved prognostication and segmentation of cancer. This study aims to employ advanced fusion techniques, …
May 17, 2022
ProjectID: P202205170003
Bronze DoneAdvanced automatic segmentation of tumors and survival prediction in head and neck cancer
In this study, 325 subjects were extracted from the HECKTOR-Challenge. 224 subjects were considered in the training procedure, and 101 subjects were employed to validate …
May 17, 2022
ProjectID: P202204050001
Bronze DonePrediction of Cognitive Decline in Parkinson's Disease using Hybrid Machine Learning and Deep Learning Techniques.
Objectives: Prediction of disease progression in PD patients holds significant value. Individual knowledge of disease progression can help to make proper social and occupational decisions …
April 5, 2022
ProjectID: P202203170004
Bronze DoneRobustness and Reproducibility of Radiomics Features from Fusions of PET-CT Images
Objectives: Radiomics is a major frontier in medical image analysis, enabling mining of high-dimensional data from images. Although radiomics features (RF) are increasingly extracted via …
March 20, 2022
ProjectID: P202203140002
Silver DoneDeep versus handcrafted tensor radiomics features: application to survival prediction in head and neck cancer
Objectives: In this study, multi-level multi-modality-fusion technique is a promising technique with potential for improved prognostication of head and neck (HN) cancer. We aim to …
March 20, 2022
ProjectID: P202103100018
Bronze DoneReliable and Reproducible Tensor Radiomics Features in Prediction of Survival in Head and Neck Cancer
Objectives: Radiomics is a major frontier in medical image analysis, enabling mining of high-dimensional data from images. Although radiomics features (RF) are increasingly extracted via …
Feb. 7, 2022
ProjectID: P202103100012
Bronze DoneLongitudinal Clustering Analysis and Prediction of Parkinson’s Disease Progression
Objectives: We aimed to identify distinct disease progression pathways in Parkinson’s disease (PD), making use of clinical and imaging features, towards improved understanding of disease …
May 18, 2021
ProjectID: P202103100011
Bronze DoneHybrid Machine Learning Methods and Ensemble Voting for Identification of Parkinson’s Disease Subtypes
Objectives: It is important to subdivide Parkinson’s disease (PD) into specific subtypes, since homogeneous groups of patients are more likely to share genetic and pathological …
Feb. 18, 2021
ProjectID: P202103100004
Bronze DoneRobust identification of Parkinson’s disease subtypes using radiomics and hybrid machine learning
Objectives: It is important to subdivide Parkinson’s disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible …
Dec. 20, 2020
ProjectID: P202102100003
Bronze DoneCognitive Outcome Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
Objective: Montreal Cognitive Assessment (MoCA) as a rapid nonmotor-screening test assesses different aspects of cognitive dysfunction. Early prediction of these symptoms may facilitate better temporal …
Sept. 1, 2021
ProjectID: P202103100002
Bronze DoneMulti-Modality Fusion Coupled with Deep Learning for Improved Outcome Prediction in Head and Neck Cancer
Objective: Multi-level multi-modality-fusion-radiomics is a promising technique with potential for improved prognostication of cancer. We aim to use advanced fusion-techniques on PET and CT images …
Aug. 1, 2021
ProjectID: P202103100001
Bronze DoneDrug Amount Prediction in Parkinson’s Disease Using Hybrid Machine Learning Systems and Radiomics Features
Objectives: Parkinson’s Disease (PD) is progressive and heterogeneous. Predicting and personalizing drug amount consistently to treat PD patients holds significant promises to enhance chances of …
Aug. 1, 2021
ProjectID: P202102100002
Bronze DonePrediction of TNM Stage in Head and Neck Cancer Using Hybrid Machine Learning Systems and …
Objective: The tumor, node, metastasis (TNM) staging system enables clinicians to describe the spread of head-and-neck-squamous-cell-carcinoma (HNSCC) cancer in a specific manner to assist with …
Aug. 1, 2021
ProjectID: P202102100001
Bronze DoneAdvanced Survival Prediction in Head and Neck Cancer Using Hybrid Machine Learning Systems and Radiomics …
Objective: Accurate prognostic stratification of Head-and-Neck-Squamous-Cell-Carcinoma (HNSCC) patients can be an important clinical reference when designing therapeutic strategies. We set to predict 4 outcomes: overall-survival …
Aug. 1, 2021