ArticleProceedings. IEEE International Conference on Computer Vision2021
Weakly Supervised 3D Semantic Segmentation Using Cross-Image Consensus and Inter-Voxel Affinity Relations.
Article in Proceedings. IEEE International Conference on Computer Vision, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- From Unsupervised to Zero-Shot: 3D Domain Adaptation for Cryo-Electron Tomography Segmentation.International journal of computer vision · 2026Article
- MVGFormer: Multi-view perspective with graph-guided transformer for cryo-ET segmentation.Knowledge-based systems · 2026Article
- A Feasibility Study on Deep Learning Based Brain Tumor Segmentation Using 2D Ellipse Box Areas.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
We propose a novel weakly supervised approach for 3D semantic segmentation on volumetric images. Unlike most existing methods that require voxel-wise densely labeled training data, our weakly-supervised CIVA-Net is the first model that only needs image-level class labels as guidance to learn accurate volumetric segmentation. Our model learns from cross-image co-occurrence for integral region generation, and explores inter-voxel affinity relations to predict segmentation with accurate boundaries. We empirically validate our model on both simulated and real cryo-ET datasets. Our experiments show that CIVA-Net achieves comparable performance to the state-of-the-art models trained with stronger supervision.
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Registered trials
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