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.
A systematic analysis of performance measures for classification tasks
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
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.
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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Synthetic Flight Data Generation Using Generative Models
Synthetic flight data generated by TVAE and Gaussian Copula models supports flight delay prediction models with accuracy comparable to real data.