Warm start active learning with proxy labels and selection via semi-supervised fine-tuning
Which volume to annotate next is a challenging problem in building medical imaging
datasets for deep learning. One of the promising methods to approach this question is active …
datasets for deep learning. One of the promising methods to approach this question is active …
Taal: Test-time augmentation for active learning in medical image segmentation
Deep learning methods typically depend on the availability of labeled data, which is
expensive and time-consuming to obtain. Active learning addresses such effort by …
expensive and time-consuming to obtain. Active learning addresses such effort by …
Diminishing uncertainty within the training pool: Active learning for medical image segmentation
Active learning is a unique abstraction of machine learning techniques where the
model/algorithm could guide users for annotation of a set of data points that would be …
model/algorithm could guide users for annotation of a set of data points that would be …
Dsal: Deeply supervised active learning from strong and weak labelers for biomedical image segmentation
Image segmentation is one of the most essential biomedical image processing problems for
different imaging modalities, including microscopy and X-ray in the Internet-of-Medical …
different imaging modalities, including microscopy and X-ray in the Internet-of-Medical …
A sparse annotation strategy based on attention-guided active learning for 3D medical image segmentation
3D image segmentation is one of the most important and ubiquitous problems in medical
image processing. It provides detailed quantitative analysis for accurate disease diagnosis …
image processing. It provides detailed quantitative analysis for accurate disease diagnosis …
HAL-IA: A Hybrid Active Learning framework using Interactive Annotation for medical image segmentation
X Li, M Xia, J Jiao, S Zhou, C Chang, Y Wang… - Medical Image …, 2023 - Elsevier
High performance of deep learning models on medical image segmentation greatly relies on
large amount of pixel-wise annotated data, yet annotations are costly to collect. How to …
large amount of pixel-wise annotated data, yet annotations are costly to collect. How to …
Density-based one-shot active learning for image segmentation
Image segmentation is a key step in image processing tasks, which has significant
applications in computer vision field such as medical image analysis, scene understanding …
applications in computer vision field such as medical image analysis, scene understanding …
Pt4al: Using self-supervised pretext tasks for active learning
Labeling a large set of data is expensive. Active learning aims to tackle this problem by
asking to annotate only the most informative data from the unlabeled set. We propose a …
asking to annotate only the most informative data from the unlabeled set. We propose a …
Parting with illusions about deep active learning
Active learning aims to reduce the high labeling cost involved in training machine learning
models on large datasets by efficiently labeling only the most informative samples. Recently …
models on large datasets by efficiently labeling only the most informative samples. Recently …
ALGES: active learning with gradient embeddings for semantic segmentation of laparoscopic surgical images
Annotating medical images for the purposes of training computer vision models is an
extremely laborious task that takes time and resources away from expert clinicians. Active …
extremely laborious task that takes time and resources away from expert clinicians. Active …