Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives
Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …
prevalence in natural language processing or computer vision. Since medical imaging bear …
Learning with limited annotations: a survey on deep semi-supervised learning for medical image segmentation
Medical image segmentation is a fundamental and critical step in many image-guided
clinical approaches. Recent success of deep learning-based segmentation methods usually …
clinical approaches. Recent success of deep learning-based segmentation methods usually …
Fast and low-GPU-memory abdomen CT organ segmentation: the flare challenge
Automatic segmentation of abdominal organs in CT scans plays an important role in clinical
practice. However, most existing benchmarks and datasets only focus on segmentation …
practice. However, most existing benchmarks and datasets only focus on segmentation …
A whole-body FDG-PET/CT dataset with manually annotated tumor lesions
We describe a publicly available dataset of annotated Positron Emission Tomography/
Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG) …
Computed Tomography (PET/CT) studies. 1014 whole body Fluorodeoxyglucose (FDG) …
[HTML][HTML] DCSAU-Net: A deeper and more compact split-attention U-Net for medical image segmentation
Deep learning architecture with convolutional neural network achieves outstanding success
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
in the field of computer vision. Where U-Net has made a great breakthrough in biomedical …
Overview of the HECKTOR challenge at MICCAI 2021: automatic head and neck tumor segmentation and outcome prediction in PET/CT images
V Andrearczyk, V Oreiller, S Boughdad… - 3D head and neck tumor …, 2021 - Springer
This paper presents an overview of the second edition of the HEad and neCK TumOR
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
(HECKTOR) challenge, organized as a satellite event of the 24th International Conference …
Current and emerging trends in medical image segmentation with deep learning
PH Conze, G Andrade-Miranda… - … on Radiation and …, 2023 - ieeexplore.ieee.org
In recent years, the segmentation of anatomical or pathological structures using deep
learning has experienced a widespread interest in medical image analysis. Remarkably …
learning has experienced a widespread interest in medical image analysis. Remarkably …
Synthetic data as an enabler for machine learning applications in medicine
Synthetic data generation is the process of using machine learning methods to train a model
that captures the patterns in a real dataset. Then new or synthetic data can be generated …
that captures the patterns in a real dataset. Then new or synthetic data can be generated …
Semi-supervised 3D-InceptionNet for segmentation and survival prediction of head and neck primary cancers
Cancers, known collectively as head and neck cancers, usually begin in the squamous cells
that line the head and neck's mucosal surfaces, forming a tumour mass. It usually develops …
that line the head and neck's mucosal surfaces, forming a tumour mass. It usually develops …
Screening for extranodal extension in HPV-associated oropharyngeal carcinoma: evaluation of a CT-based deep learning algorithm in patient data from a multicentre …
Background Pretreatment identification of pathological extranodal extension (ENE) would
guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated …
guide therapy de-escalation strategies for in human papillomavirus (HPV)-associated …