Challenges for machine learning in clinical translation of big data imaging studies
Combining deep learning image analysis methods and large-scale imaging datasets offers
many opportunities to neuroscience imaging and epidemiology. However, despite these …
many opportunities to neuroscience imaging and epidemiology. However, despite these …
SDC-UDA: volumetric unsupervised domain adaptation framework for slice-direction continuous cross-modality medical image segmentation
Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
nearly human-level performance in fully supervised manner. However, acquiring pixel-level …
CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation
Abstract Domain Adaptation (DA) has recently been of strong interest in the medical imaging
community. While a large variety of DA techniques have been proposed for image …
community. While a large variety of DA techniques have been proposed for image …
Segmentation of vestibular schwannoma from MRI, an open annotated dataset and baseline algorithm
Automatic segmentation of vestibular schwannomas (VS) from magnetic resonance imaging
(MRI) could significantly improve clinical workflow and assist patient management. We have …
(MRI) could significantly improve clinical workflow and assist patient management. We have …
Contrastive semi-supervised learning for domain adaptive segmentation across similar anatomical structures
Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance for
medical image segmentation, yet need plenty of manual annotations for training. Semi …
medical image segmentation, yet need plenty of manual annotations for training. Semi …
Source-free domain adaptation for image segmentation
Abstract Domain adaptation (DA) has drawn high interest for its capacity to adapt a model
trained on labeled source data to perform well on unlabeled or weakly labeled target data …
trained on labeled source data to perform well on unlabeled or weakly labeled target data …
Scribble-based boundary-aware network for weakly supervised salient object detection in remote sensing images
Existing CNN-based salient object detection (SOD) models rely heavily on large-scale pixel-
level annotations, which are labor-intensive, time-consuming, and expensive. In contrast …
level annotations, which are labor-intensive, time-consuming, and expensive. In contrast …
Label-efficient deep learning in medical image analysis: Challenges and future directions
Deep learning has seen rapid growth in recent years and achieved state-of-the-art
performance in a wide range of applications. However, training models typically requires …
performance in a wide range of applications. However, training models typically requires …
COSMOS: cross-modality unsupervised domain adaptation for 3D medical image segmentation based on target-aware domain translation and iterative self-training
Recent advances in deep learning-based medical image segmentation studies achieve
nearly human-level performance when in fully supervised condition. However, acquiring …
nearly human-level performance when in fully supervised condition. However, acquiring …
Scribble2d5: Weakly-supervised volumetric image segmentation via scribble annotations
Image segmentation using weak annotations like scribbles has gained great attention, since
such annotations are easier to obtain compared to time-consuming and labor-intensive …
such annotations are easier to obtain compared to time-consuming and labor-intensive …