REVIEW 6 cited by
Augmenting Medical Imaging: A Comprehensive Catalogue of 65 Techniques for Enhanced Data Analysis
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
In the realm of medical imaging, the training of machine learning models necessitates a large and varied training dataset to ensure robustness and interoperability. However, acquiring such diverse and heterogeneous data can be difficult due to the need for expert labeling of each image and privacy concerns associated with medical data. To circumvent these challenges, data augmentation has emerged as a promising and cost-effective technique for increasing the size and diversity of the training dataset. In this study, we provide a comprehensive review of the specific data augmentation techniques employed in medical imaging and explore their benefits. We conducted an in-depth study of all data augmentation techniques used in medical imaging, identifying 11 different purposes and collecting 65 distinct techniques. The techniques were operationalized into spatial transformation-based, color and contrast adjustment-based, noise-based, deformation-based, data mixing-based, filters and mask-based, division-based, multi-scale and multi-view-based, and meta-learning-based categories. We observed that some techniques require manual specification of all parameters, while others rely on automation to adjust the type and magnitude of augmentation based on task requirements. The utilization of these techniques enables the development of more robust models that can be applied in domains with limited or challenging data availability. It is expected that the list of available techniques will expand in the future, providing researchers with additional options to consider.
Forward citations
Cited by 6 Pith papers
-
Not All Pixels Are Equal: Pixel-wise Meta-Learning for Medical Segmentation with Noisy Labels
MetaDCSeg improves medical segmentation under noisy labels by combining meta-learned pixel weights with a dynamic boundary-distance attention module.
-
pyMEAL: A Multi-Encoder Augmentation-Aware-Learning Toolbox for Robust Medical Image Translation
A multi-encoder, augmentation-aware architecture with attention-weighted fusion (MEAL-BD) is claimed to improve robustness of 3D CT-to-T1 MRI translation over single-stream baselines.
-
ConDSeg: A General Medical Image Segmentation Framework via Contrast-Driven Feature Enhancement
ConDSeg, a contrast-driven medical image segmentation framework, reports higher IoU and Dice scores than prior methods on five public datasets across three imaging modalities.
-
Faithful, Interpretable Chest X-ray Diagnosis with Anti-Aliased B-cos Networks
Combining B-cos networks with anti-aliasing pooling (FLC or BlurPool) reduces grid artifacts in chest X-ray explanation maps while keeping diagnostic accuracy close to baseline networks.
-
Cancer cytoplasm segmentation in hyperspectral cell image with data augmentation
Pseudo-hyperspectral images made from CMOS photos plus synthetic line noise improve cancer cytoplasm segmentation IoU from 0.7255 to 0.7362 on a 56-image test set.
-
Improving text-conditioned latent diffusion for cancer pathology
On TCGA-BRCA, a latent diffusion model conditioned on 35-token pathology summaries achieved FID 21.11 versus 22.39 for the authors' 154-token PathLDM reproduction.
Discussion (0). Continue with ORCID to comment.