Introduces EchoRisk, the first curated multicentre longitudinal echocardiography dataset with cardiotoxicity labels, plus three benchmark tasks and a public baseline for cardio-oncology AI.
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A Transformer-based patient-specific non-rigid point cloud registration pipeline with overlap estimation and physics-based refinement outperforms generic methods on synthetic laparoscopic data.
A dual-query scene graph generation method unifies detector-based and query-based reasoning in a single decoder, achieving state-of-the-art results on Visual Genome, Open Images v6, and GQA-200.
DAPR is a model-agnostic plug-in that rebalances gradient contributions across head and tail classes and applies multi-scale distance search for distributional compactness, improving VPR performance by 18.3% on SF-XL v1 and 6.7% on v2.
Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.
citing papers explorer
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EchoRisk: A Multicentre Echocardiography Dataset and Benchmark for Cardio-Oncology
Introduces EchoRisk, the first curated multicentre longitudinal echocardiography dataset with cardiotoxicity labels, plus three benchmark tasks and a public baseline for cardio-oncology AI.
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Towards Patient-Specific Deformable Registration in Laparoscopic Surgery
A Transformer-based patient-specific non-rigid point cloud registration pipeline with overlap estimation and physics-based refinement outperforms generic methods on synthetic laparoscopic data.
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Revisiting Scene Graph Generation from the Perspective of Detector-Conditioned Reachability
A dual-query scene graph generation method unifies detector-based and query-based reasoning in a single decoder, achieving state-of-the-art results on Visual Genome, Open Images v6, and GQA-200.
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Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition
DAPR is a model-agnostic plug-in that rebalances gradient contributions across head and tail classes and applies multi-scale distance search for distributional compactness, improving VPR performance by 18.3% on SF-XL v1 and 6.7% on v2.
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The Learnability Gap in Medical Latent Diffusion
Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.