ArticleMedical image analysis2022
STAMP: Simultaneous Training and Model Pruning for low data regimes in medical image segmentation.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Lean Unet: a compact model for image segmentation.International journal of computer assisted radiology and surgery · 2026Article
- Deep Learning-Driven Innovations in Echocardiography: Taxonomy, Clinical Impact, Challenges, and Opportunities.Annals of biomedical engineering · 2026Review
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- Lightweight U-Net for Blood Vessels Segmentation in X-Ray Coronary Angiography.Journal of imaging · 2025Article
- Article
- How Does Pruning Impact Long-Tailed Multi-label Medical Image Classifiers?Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Acquisition of high quality manual annotations is vital for the development of segmentation algorithms. However, to create them we require a substantial amount of expert time and knowledge. Large numbers of labels are required to train convolutional neural networks due to the vast number of parameters that must be learned in the optimisation process. Here, we develop the STAMP algorithm to allow the simultaneous training and pruning of a UNet architecture for medical image segmentation with targeted channelwise dropout to make the network robust to the pruning. We demonstrate the technique across segmentation tasks and imaging modalities. It is then shown that, through online pruning, we are able to train networks to have much higher performance than the equivalent standard UNet models while reducing their size by more than 85% in terms of parameters. This has the potential to allow networks to be directly trained on datasets where very low numbers of labels are available.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.