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From the 1 of 10 linked papers with an AI index.

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10 papers

cs.CV2026

GB-SVFBP: Gaussian-Based Shift-Variant FBP neural network

Chengze Ye, Linda-Sophie Schneider, Yipeng Sun +1

The paper introduces a Gaussian-based shift-variant filtered backprojection neural network that efficiently reconstructs cone-beam CT images from non-circular trajectories with far…

eess.IV2026

Filter2Noise: A Framework for Interpretable and Zero-Shot Low-Dose CT Image Denoising

Yipeng Sun, Linda-Sophie Schneider, Siyuan Mei +8

Noise in low-dose computed tomography (LDCT) can obscure important diagnostic details. While deep learning offers powerful denoising, supervised methods require impractical paired…

cs.CV2026

Unbalanced optimal transport for robust longitudinal lesion evolution with registration-aware and appearance-guided priors

Melika Qahqaie, Dominik Neumann, Tobias Heimann +2

Evaluating lesion evolution in longitudinal CT scans of can cer patients is essential for assessing treatment response, yet establishing reliable lesion correspondence across time…

cs.CV2026

ProGiDiff: Prompt-Guided Diffusion-Based Medical Image Segmentation

Yuan Lin, Murong Xu, Marc Hölle +5

Widely adopted medical image segmentation methods, although efficient, are primarily deterministic and remain poorly amenable to natural language prompts. Thus, they lack the capab…

cs.CV2025

Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals

Susu Sun, Stefano Woerner, Andreas Maier +2

Interpretability is crucial for machine learning algorithms in high-stakes medical applications. However, high-performing neural networks typically cannot explain their predictions…

cs.LG2025

An update to PYRO-NN: A Python Library for Differentiable CT Operators

Linda-Sophie Schneider, Yipeng Sun, Chengze Ye +2

Deep learning has brought significant advancements to X-ray Computed Tomography (CT) reconstruction, offering solutions to challenges arising from modern imaging technologies. Thes…