ArticlePattern recognition letters2020
Multi-region saliency-aware learning for cross-domain placenta image segmentation.
Article in Pattern recognition letters, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Article
- Applying AI models to digital placental photographs to automate and improve morphology assessments.medRxiv : the preprint server for health sciences · 2026Article
- Automatic bridge crack detections: A lightweight segmentation model for UAV inspection.PloS one · 2026Article
- Cross-modal contrastive learning for unified placenta analysis using photographs.Patterns (New York, N.Y.) · 2024Article
- A Multi-Stage Feature Aggregation and Structure Awareness Network for Concrete Bridge Crack Detection.Sensors (Basel, Switzerland) · 2024Article
- Enhancing Automatic Placenta Analysis through Distributional Feature Recomposition in Vision-Language Contrastive Learning.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2023Article
- Deep learning based automatic segmentation of the placenta and uterine cavity on prenatal MR images.Proceedings of SPIE--the International Society for Optical Engineering · 2023Article
- Article
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Authors and funding
6 authors.
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Abstract
We propose a multi-region saliency-aware learning (MSL) method for cross-domain placenta image segmentation. Unlike most existing image-level transfer learning methods that fail to preserve the semantics of paired regions, our MSL incorporates the attention mechanism and a saliency constraint into the adversarial translation process, which can realize multi-region mappings in the semantic level. Specifically, the built-in attention module serves to detect the most discriminative semantic regions that the generator should focus on. Then we use the attention consistency as another guidance for retaining semantics after translation. Furthermore, we exploit the specially designed saliency-consistent constraint to enforce the semantic consistency by requiring the saliency regions unchanged. We conduct experiments using two real-world placenta datasets we have collected. We examine the efficacy of this approach in (1) segmentation and (2) prediction of the placental diagnoses of fetal and maternal inflammatory response (FIR, MIR). Experimental results show the superiority of the proposed approach over the state of the art.
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