AuraMask produces 40 aesthetic anti-facial recognition filters that match or exceed prior adversarial effectiveness and achieve significantly higher user acceptance in a 630-person study.
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Attention U-Net: Learning Where to Look for the Pancreas
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abstract
We propose a novel attention gate (AG) model for medical imaging that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input image while highlighting salient features useful for a specific task. This enables us to eliminate the necessity of using explicit external tissue/organ localisation modules of cascaded convolutional neural networks (CNNs). AGs can be easily integrated into standard CNN architectures such as the U-Net model with minimal computational overhead while increasing the model sensitivity and prediction accuracy. The proposed Attention U-Net architecture is evaluated on two large CT abdominal datasets for multi-class image segmentation. Experimental results show that AGs consistently improve the prediction performance of U-Net across different datasets and training sizes while preserving computational efficiency. The code for the proposed architecture is publicly available.
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representative citing papers
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XAttnRes introduces cross-stage attention residuals that maintain a global feature history and selectively aggregate prior representations, improving medical image segmentation and performing on par with baselines even without skip connections.
GDLA delivers state-of-the-art accuracy on CT, MRI, ultrasound and dermoscopy segmentation benchmarks while keeping linear O(N) complexity in a PVT encoder-decoder.
Variational Regularization imposes an adaptive information bottleneck on noisy intermediate features in DP3-UNet and DP3-DiT policies, consistently raising task success rates on RoboTwin2.0, Adroit, and MetaWorld while achieving new state-of-the-art results.
S2M-Net achieves state-of-the-art Dice scores on 16 medical datasets across 8 modalities using a 4.7M-parameter spectral-spatial mixer and morphology-aware adaptive loss, outperforming transformers with 3.5-6x fewer parameters.
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A spectral vision transformer achieves equitable or superior performance with fewer parameters than standard ViTs, CNNs, and other models by using spectral projections for tokenization in limited-data medical imaging.
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SPD improves SAM segmentation robustness to noisy prompts by learning anatomical saliency priors, distilling consensus prompts from adjacent slices, and enforcing pairwise slice consistency.
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CDSA-Net decouples vascular structure extraction and background restoration in coronary DSA via hierarchical geometric priors and adaptive noise modeling to eliminate artifacts while preserving tissue fidelity.
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The paper defines the Conformal Hallucination Estimation Metric (CHEM) that localizes hallucination-prone regions in image reconstruction models via multiscale representations and distribution-free conformal regression.
NTRM combines CNNs with tissue-level graph neural networks to model inter-tissue relationships, delivering 4.9% to 31.25% higher Dice scores than prior methods on a non-melanoma skin cancer histology segmentation benchmark.
SAMRI fine-tunes only the mask decoder of SAM on 1.1 million MRI slices from 30 datasets to reach mean DSC 0.87 on 47 targets and strong zero-shot performance.
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citing papers explorer
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FEFormer: Frequency-enhanced Vision Transformer for Generic Knowledge Extraction and Adaptive Feature Fusion in Volumetric Medical Image Segmentation
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ESICA: A Scalable Framework for Text-Guided 3D Medical Image Segmentation
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Mapping License Plate Recoverability Under Extreme Viewing Angles for Oppor-tunistic Urban Sensing
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Learning from Noisy Prompts: Saliency-Guided Prompt Distillation for Robust Segmentation with SAM
SPD improves SAM segmentation robustness to noisy prompts by learning anatomical saliency priors, distilling consensus prompts from adjacent slices, and enforcing pairwise slice consistency.
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Toward Polymorphic Backdoor against Semantic Communication via Intensity-Based Poisoning
SemBugger achieves polymorphic backdoors in semantic communication via graded-intensity trigger poisoning and hierarchical loss, plus a noise-based defense with a theoretical efficacy bound.
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CDSA-Net:Collaborative Decoupling of Vascular Structure and Background for High-Fidelity Coronary Digital Subtraction Angiography
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Category-based Galaxy Image Generation via Diffusion Models
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Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation
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Clinical utility of foundation models in musculoskeletal MRI for biomarker fidelity and predictive outcomes
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Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs
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Attention-ResUNet for Automated Fetal Head Segmentation
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Adaptive Dual Residual U-Net with Attention Gate and Multiscale Spatial Attention Mechanisms (ADRUwAMS)
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