:: Library Catalog

Ata uhi

I tiakina i:

Ngā taipitopito rārangi puna kōrero
Ngā kaituhi matua:	Di Feola, Francesco, Tronchin, Lorenzo, Guarrasi, Valerio, Soda, Paolo
Hōputu:	Preprint
I whakaputaina:	2024
Ngā marau:	Image and Video Processing Computer Vision and Pattern Recognition Machine Learning
Urunga tuihono:	https://arxiv.org/abs/2403.16640
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Ngā tūemi rite

Texture-Aware StarGAN for CT data harmonisation
mā: Di Feola, Francesco, me ētahi atu.
I whakaputaina: (2025)

Multi-Dataset Multi-Task Learning for COVID-19 Prognosis
mā: Ruffini, Filippo, me ētahi atu.
I whakaputaina: (2024)

Multi-objective optimization determines when, which and how to fuse deep networks: an application to predict COVID-19 outcomes
mā: Guarrasi, Valerio, me ētahi atu.
I whakaputaina: (2022)

Whole-Body Image-to-Image Translation for a Virtual Scanner in a Healthcare Digital Twin
mā: Guarrasi, Valerio, me ētahi atu.
I whakaputaina: (2025)

Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging
mā: Ayllón, Elena Mulero, me ētahi atu.
I whakaputaina: (2025)

Doctor-in-the-Loop: An Explainable, Multi-View Deep Learning Framework for Predicting Pathological Response in Non-Small Cell Lung Cancer
mā: Caragliano, Alice Natalina, me ētahi atu.
I whakaputaina: (2025)

Augmented Intelligence for Multimodal Virtual Biopsy in Breast Cancer Using Generative Artificial Intelligence
mā: Rofena, Aurora, me ētahi atu.
I whakaputaina: (2025)

A Deep Learning Approach for Virtual Contrast Enhancement in Contrast Enhanced Spectral Mammography
mā: Rofena, Aurora, me ētahi atu.
I whakaputaina: (2023)

Asymmetric GANs for Image-to-Image Translation
mā: Tang, Hao, me ētahi atu.
I whakaputaina: (2019)

Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains
mā: Salmè, Marco, me ētahi atu.
I whakaputaina: (2025)

ViTGAN: Training GANs with Vision Transformers
mā: Lee, Kwonjoon, me ētahi atu.
I whakaputaina: (2021)

pcaGAN: Improving Posterior-Sampling cGANs via Principal Component Regularization
mā: Bendel, Matthew C., me ētahi atu.
I whakaputaina: (2024)

Self-supervised denoising of visual field data improves detection of glaucoma progression
mā: Wu, Sean, me ētahi atu.
I whakaputaina: (2024)

Brain tumor segmentation using synthetic MR images -- A comparison of GANs and diffusion models
mā: Akbar, Muhammad Usman, me ētahi atu.
I whakaputaina: (2023)

Self-supervised training of deep denoisers in multi-coil MRI considering noise correlations
mā: Park, Juhyung, me ētahi atu.
I whakaputaina: (2022)

TADM: Temporally-Aware Diffusion Model for Neurodegenerative Progression on Brain MRI
mā: Litrico, Mattia, me ētahi atu.
I whakaputaina: (2024)

Using Multi-Temporal Sentinel-1 and Sentinel-2 data for water bodies mapping
mā: Russo, Luigi, me ētahi atu.
I whakaputaina: (2024)

Memory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy
mā: Ugolkov, Evgeny, me ētahi atu.
I whakaputaina: (2025)

One Shot GANs for Long Tail Problem in Skin Lesion Dataset using novel content space assessment metric
mā: Deo, Kunal, me ētahi atu.
I whakaputaina: (2024)

Local Padding in Patch-Based GANs for Seamless Infinite-Sized Texture Synthesis
mā: Abdellatif, Alhasan, me ētahi atu.
I whakaputaina: (2023)

3D Wasserstein generative adversarial network with dense U-Net based discriminator for preclinical fMRI denoising
mā: Soltanpour, Sima, me ētahi atu.
I whakaputaina: (2024)

Multi-task learning for classification, segmentation, reconstruction, and detection on chest CT scans
mā: Hryniewska-Guzik, Weronika, me ētahi atu.
I whakaputaina: (2023)

Multi-modal Evidential Fusion Network for Trustworthy PET/CT Tumor Segmentation
mā: Qi, Yuxuan, me ētahi atu.
I whakaputaina: (2024)

Beware of diffusion models for synthesizing medical images -- A comparison with GANs in terms of memorizing brain MRI and chest x-ray images
mā: Akbar, Muhammad Usman, me ētahi atu.
I whakaputaina: (2023)

Untrained Perceptual Loss for image denoising of line-like structures in MR images
mā: Pfaehler, Elisabeth, me ētahi atu.
I whakaputaina: (2024)

MedMNIST-C: Comprehensive benchmark and improved classifier robustness by simulating realistic image corruptions
mā: Di Salvo, Francesco, me ētahi atu.
I whakaputaina: (2024)

Self-supervised Vision Transformer are Scalable Generative Models for Domain Generalization
mā: Doerrich, Sebastian, me ētahi atu.
I whakaputaina: (2024)

Unsupervised Feature Orthogonalization for Learning Distortion-Invariant Representations
mā: Doerrich, Sebastian, me ētahi atu.
I whakaputaina: (2024)

BeyondCT: A deep learning model for predicting pulmonary function from chest CT scans
mā: Geng, Kaiwen, me ētahi atu.
I whakaputaina: (2024)

Multi-View Transformers for Airway-To-Lung Ratio Inference on Cardiac CT Scans: The C4R Study
mā: Naik, Sneha N., me ētahi atu.
I whakaputaina: (2025)

AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks
mā: Qu, Chongyu, me ētahi atu.
I whakaputaina: (2023)

Lesion-Aware Cross-Phase Attention Network for Renal Tumor Subtype Classification on Multi-Phase CT Scans
mā: Uhm, Kwang-Hyun, me ētahi atu.
I whakaputaina: (2024)

CT-3DFlow : Leveraging 3D Normalizing Flows for Unsupervised Detection of Pathological Pulmonary CT scans
mā: Djahnine, Aissam, me ētahi atu.
I whakaputaina: (2024)

AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
mā: Meng, Mingyuan, me ētahi atu.
I whakaputaina: (2023)

An Embedding is Worth a Thousand Noisy Labels
mā: Di Salvo, Francesco, me ētahi atu.
I whakaputaina: (2024)

Privacy-preserving datasets by capturing feature distributions with Conditional VAEs
mā: Di Salvo, Francesco, me ētahi atu.
I whakaputaina: (2024)

HypCBC: Domain-Invariant Hyperbolic Cross-Branch Consistency for Generalizable Medical Image Analysis
mā: Di Salvo, Francesco, me ētahi atu.
I whakaputaina: (2026)

Embedding-Based Federated Data Sharing via Differentially Private Conditional VAEs
mā: Di Salvo, Francesco, me ētahi atu.
I whakaputaina: (2025)

Stylizing ViT: Anatomy-Preserving Instance Style Transfer for Domain Generalization
mā: Doerrich, Sebastian, me ētahi atu.
I whakaputaina: (2026)

Rethinking model prototyping through the MedMNIST+ dataset collection
mā: Doerrich, Sebastian, me ētahi atu.
I whakaputaina: (2024)