4 papers
MADPOT: Medical Anomaly Detection with CLIP Adaptation and Partial Optimal Transport
Mahshid Shiri, Cigdem Beyan, Vittorio Murino
Medical anomaly detection (AD) is challenging due to diverse imaging modalities, anatomical variations, and limited labeled data. We propose a novel approach combining visual adapt…
MadCLIP: Few-shot Medical Anomaly Detection with CLIP
Mahshid Shiri, Cigdem Beyan, Vittorio Murino
An innovative few-shot anomaly detection approach is presented, leveraging the pre-trained CLIP model for medical data, and adapting it for both image-level anomaly classification…
Memory-Efficient 3D High-Resolution Medical Image Synthesis Using CRF-Guided GANs
Mahshid Shiri, Alessandro Bruno, Daniele Loiacono
Generative Adversarial Networks (GANs) have many potential medical imaging applications. Due to the limited memory of Graphical Processing Units (GPUs), most current 3D GAN models…
Comparative clinical evaluation of "memory-efficient" synthetic 3d generative adversarial networks (gan) head-to-head to state of art: results on computed tomography of the chest
Mahshid Shiri, Chandra Bortolotto, Alessandro Bruno +5
Generative Adversarial Networks (GANs) are increasingly used to generate synthetic medical images, addressing the critical shortage of annotated data for training Artificial Intell…