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Found 15 papers, 0 papers with code
The Brain Tumor Segmentation in Pediatrics (BraTS-PEDs) Challenge: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
A multi-institutional pediatric dataset of clinical radiology MRIs by the Children's Brain Tumor Network
Pediatric brain and spinal cancers remain the leading cause of cancer-related death in children.
Harmonization Across Imaging Locations(HAIL): One-Shot Learning for Brain MRI
To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization.
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare.
SelfFed: Self-supervised Federated Learning for Data Heterogeneity and Label Scarcity in IoMT
We perform our experimental analysis on publicly available medical imaging datasets and show that our proposed SelfFed framework performs better when compared to existing baselines concerning non-independent and identically distributed (IID) data and label scarcity.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)
Pediatric tumors of the central nervous system are the most common cause of cancer-related death in children.
The Brain Tumor Segmentation (BraTS) Challenge 2023: Brain MR Image Synthesis for Tumor Segmentation (BraSyn)
In this work, we present the establishment of the Brain MR Image Synthesis Benchmark (BraSyn) in conjunction with the Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2023.
The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma
Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality.
Biomedical image analysis competitions: The state of current participation practice
Of these, 84% were based on standard architectures.
SPCXR: Self-supervised Pretraining using Chest X-rays Towards a Domain Specific Foundation Model
However, the traditional diagnostic tool design methods based on supervised learning are burdened by the need to provide training data annotation, which should be of good quality for better clinical outcomes.
Ranked #1 on
Semantic Segmentation
on Montgomery County X-ray Set
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs
We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet.
Communal Domain Learning for Registration in Drifted Image Spaces
Designing a registration framework for images that do not share the same probability distribution is a major challenge in modern image analytics yet trivial task for the human visual system (HVS).
Region Proposal Networks with Contextual Selective Attention for Real-Time Organ Detection
Our novel selective attention approach (i) effectively shrinks the search space inside the feature map, (ii) appends useful localization information to the hypothesized proposal for the detection architecture to learn where to look for each organ, and (iii) modifies the pyramid of regression references in the RPN by incorporating organ- and modality-specific information, which results in additional time reduction.
Computational Anatomy for Multi-Organ Analysis in Medical Imaging: A Review
With more than 300 papers included in this review, we reflect on the trends and challenges of the field of computational anatomy, the particularities of each anatomical region, and the potential of multi-organ analysis to increase the impact of 35 medical imaging applications on the future of healthcare.
A Generic Approach to Lung Field Segmentation from Chest Radiographs using Deep Space and Shape Learning
The main contributions of our work are: (1) a generic lung field segmentation framework from CXR accommodating large shape variation for adult and pediatric cohorts; (2) a deep representation learning detection mechanism, \emph{ensemble space learning}, for robust object localization; and (3) \emph{marginal shape deep learning} for the shape deformation parameter estimation.
