Autoencoders for unsupervised anomaly segmentation in brain MR images: a comparative study
Deep unsupervised representation learning has recently led to new approaches in the field
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …
of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these …
AI-based reconstruction for fast MRI—A systematic review and meta-analysis
Compressed sensing (CS) has been playing a key role in accelerating the magnetic
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
resonance imaging (MRI) acquisition process. With the resurgence of artificial intelligence …
Federated learning enables big data for rare cancer boundary detection
Although machine learning (ML) has shown promise across disciplines, out-of-sample
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
generalizability is concerning. This is currently addressed by sharing multi-site data, but …
The rsna-asnr-miccai brats 2021 benchmark on brain tumor segmentation and radiogenomic classification
The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Radiological Society of North America (RSNA), the American Society of Neuroradiology …
Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data
Several studies underscore the potential of deep learning in identifying complex patterns,
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
leading to diagnostic and prognostic biomarkers. Identifying sufficiently large and diverse …
Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge
Gliomas are the most common primary brain malignancies, with different degrees of
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, ie …
Advancing the cancer genome atlas glioma MRI collections with expert segmentation labels and radiomic features
Gliomas belong to a group of central nervous system tumors, and consist of various sub-
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for …
Standardization of brain MR images across machines and protocols: bridging the gap for MRI-based radiomics
Radiomics relies on the extraction of a wide variety of quantitative image-based features to
provide decision support. Magnetic resonance imaging (MRI) contributes to the …
provide decision support. Magnetic resonance imaging (MRI) contributes to the …
Deep autoencoding models for unsupervised anomaly segmentation in brain MR images
Reliably modeling normality and differentiating abnormal appearances from normal cases is
a very appealing approach for detecting pathologies in medical images. A plethora of such …
a very appealing approach for detecting pathologies in medical images. A plethora of such …
Training confounder-free deep learning models for medical applications
The presence of confounding effects (or biases) is one of the most critical challenges in
using deep learning to advance discovery in medical imaging studies. Confounders affect …
using deep learning to advance discovery in medical imaging studies. Confounders affect …