PromptDx adds a differentiable adapter to align multimodal data with a pre-trained TabPFN-style ICL engine, achieving strong Alzheimer's diagnosis performance with only 1% context samples.
In: Proceedings of the IEEE conference on computer vision and pattern recognition
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Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
HGNN combines fixed-connection CGNN and adaptive-connection IGNN branches with a graph pooling-unpooling module to achieve state-of-the-art EEG-based depression detection on two public datasets.
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.
citing papers explorer
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PromptDx: Differentiable Prompt Tuning for Multimodal In-Context Alzheimer's Diagnosis
PromptDx adds a differentiable adapter to align multimodal data with a pre-trained TabPFN-style ICL engine, achieving strong Alzheimer's diagnosis performance with only 1% context samples.
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Lighting-aware Unified Model for Instance Segmentation
Introduces LCA dual-branch adapter and pairwise loss for lighting-robust SAM instance segmentation, validated on existing benchmarks plus a new Unity synthetic dataset.
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JANUS: Anatomy-Conditioned Gating for Robust CT Triage Under Distribution Shift
JANUS conditions Vision Transformer embeddings on macro-radiomic priors via anatomically guided gating, reaching macro-AUROC 0.88 on an internal test set of 5082 cases and 0.87 on an external set of 2000 cases while improving calibration and reducing high-confidence false positives under domainshift
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A Hybrid Graph Neural Network for Enhanced EEG-Based Depression Detection
HGNN combines fixed-connection CGNN and adaptive-connection IGNN branches with a graph pooling-unpooling module to achieve state-of-the-art EEG-based depression detection on two public datasets.
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An Efficient Solution for Breast Tumor Segmentation and Classification in Ultrasound Images Using Deep Adversarial Learning
cGAN with atrous convolutions and channel weighting segments breast tumors in ultrasound at 93.76% Dice and 88.82% IoU, then classifies benign vs malignant at 85% accuracy using boundary shape features.