HFS-TriNet applies heuristic frame selection and a three-branch network (ResNet50, SAM-based with temporal attention, WTCR) to classify prostate cancer from TRUS videos.
Temporal segment networks: Towards good practices for deep action recognition
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
An O-A-R model driven adaptive hierarchical transmission system for multimodal semantic communication achieves over 90% bandwidth savings at 1-3 kbps and eliminates cliff effects in deep fading channels by sending decision-oriented semantic graphs rather than pixels.
citing papers explorer
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HFS-TriNet: A Three-Branch Collaborative Feature Learning Network for Prostate Cancer Classification from TRUS Videos
HFS-TriNet applies heuristic frame selection and a three-branch network (ResNet50, SAM-based with temporal attention, WTCR) to classify prostate cancer from TRUS videos.
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Object-Attribute-Relation Model Driven Adaptive Hierarchical Transmission for Multimodal Semantic Communication
An O-A-R model driven adaptive hierarchical transmission system for multimodal semantic communication achieves over 90% bandwidth savings at 1-3 kbps and eliminates cliff effects in deep fading channels by sending decision-oriented semantic graphs rather than pixels.