[HTML][HTML] Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation.
K Chaitanya, E Erdil, N Karani… - Medical Image Analysis, 2023 - europepmc.org
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani, E Konukoglu - arXiv preprint arXiv …, 2021 - arxiv.org
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani… - Medical Image …, 2023 - research-collection.ethz.ch
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani… - Medical image …, 2023 - pubmed.ncbi.nlm.nih.gov
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani, E Konukoglu - arXiv e-prints, 2021 - ui.adsabs.harvard.edu
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
[PDF][PDF] Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani… - Medical Image …, 2023 - researchgate.net
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
Local contrastive loss with pseudo-label based self-training for semi-supervised medical image segmentation
K Chaitanya, E Erdil, N Karani, E Konukoglu - arXiv, 2021 - research-collection.ethz.ch
Supervised deep learning-based methods yield accurate results for medical image
segmentation. However, they require large labeled datasets for this, and obtaining them is a …
segmentation. However, they require large labeled datasets for this, and obtaining them is a …