Thyroid Nodule Segmentation in Ultrasound Images with Conformal Uncertainty Estimation
Ultrasound imaging is the primary diagnostic modality, and accurate nodule segmentation is a prerequisite for CAD systems to classify malignancy and assess metastatic risk.\\ In this work, we propose a thyroid nodule segmentation framework that integrates a DeepLabV3+, channel-attention modules and post-hoc conformal prediction, providing both accurate mask and pixel-level uncertainty estimates.
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Key points
- Thyroid nodules affect a large proportion of the general population.
- Although most are benign, a non-negligible fraction is malignant.
- We address thyroid ultrasound nodule segmentation as the first stage of a pipeline that also includes malignancy classification and metastatic risk prediction.
- We adopt DeepLabV3+, whose Atrous Spatial Pyramid Pooling (ASPP) captures multi-scale context, with an EfficientNet-B5 backbone for its accuracy-efficiency trade-off.
Sources (1)
- [1]Thyroid Nodule Segmentation in Ultrasound Images with Conformal Uncertainty EstimationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 01:51 PM
Ultrasound imaging is the primary diagnostic modality, and accurate nodule segmentation is a prerequisite for CAD systems to classify malignancy and assess metastatic risk.\\ In this work, we propose a thyroid nodule segmentation framework that integrates a DeepLabV3+, channel-attention modules and post-hoc conformal prediction, providing both accurate mask and pixel-level uncertainty estimates.
Thyroid nodules affect a large proportion of the general population.
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