{"total":26,"rows":[{"id":"2204.10836","title":"Federated Learning Enables Big Data for Rare Cancer Boundary Detection","created":"2022-04-22","cited_by_count":84,"area":null},{"id":"2102.02734","title":"Deep learning-based synthetic-CT generation in radiotherapy and PET: a review","created":"2021-02-04","cited_by_count":28,"area":null},{"id":"2103.01006","title":"GaNDLF: A Generally Nuanced Deep Learning Framework for Scalable End-to-End Clinical Workflows in Medical Imaging","created":"2021-02-26","cited_by_count":25,"area":null},{"id":"2103.03158","title":"A Structural Causal Model for MR Images of Multiple Sclerosis","created":"2021-03-04","cited_by_count":21,"area":null},{"id":"2203.03626","title":"Coordinate Translator for Learning Deformable Medical Image Registration","created":"2022-03-05","cited_by_count":20,"area":null},{"id":"2405.08431","title":"Similarity and Quality Metrics for MR Image-To-Image Translation","created":"2024-05-14","cited_by_count":20,"area":null},{"id":"2212.06065","title":"HACA3: A Unified Approach for Multi-site MR Image Harmonization","created":"2022-12-12","cited_by_count":18,"area":null},{"id":"2403.13319","title":"HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling","created":"2024-03-20","cited_by_count":15,"area":null},{"id":"2505.24160","title":"Beyond the LUMIR challenge: The pathway to foundational registration models","created":"2025-05-30","cited_by_count":12,"area":null},{"id":"2407.10209","title":"Vector Field Attention for Deformable Image Registration","created":"2024-07-14","cited_by_count":10,"area":null},{"id":"2205.04329","title":"SAN-Net: Learning Generalization to Unseen Sites for Stroke Lesion Segmentation with Self-Adaptive Normalization","created":"2022-05-09","cited_by_count":7,"area":null},{"id":"2208.12491","title":"Deformation equivariant cross-modality image synthesis with paired non-aligned training data","created":"2022-08-26","cited_by_count":5,"area":null},{"id":"2310.12646","title":"TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research","created":"2023-10-19","cited_by_count":3,"area":null},{"id":"2112.02164","title":"Bridging the gap between prostate radiology and pathology through machine learning","created":"2021-12-03","cited_by_count":2,"area":null},{"id":"2207.14776","title":"Open-radiomics: A Collection of Standardized Datasets and a Technical Protocol for Reproducible Radiomics Machine Learning Pipelines","created":"2022-07-29","cited_by_count":2,"area":null},{"id":"2209.08933","title":"Estimating Brain Age with Global and Local Dependencies","created":"2022-09-19","cited_by_count":2,"area":null},{"id":"2602.00102","title":"Radiomics in Medical Imaging: Methods, Applications, and Challenges","created":"2026-01-24","cited_by_count":1,"area":null},{"id":"2403.09414","title":"Region-based U-net for accelerated training and enhanced precision in deep brain segmentation","created":"2024-03-14","cited_by_count":0,"area":null},{"id":"2405.14590","title":"MAMOC: MRI Motion Correction via Masked Autoencoding","created":"2024-05-23","cited_by_count":0,"area":null},{"id":"2409.00442","title":"Separation of Body and Background in Radiological Images. A Practical Python Code","created":"2024-08-31","cited_by_count":0,"area":null},{"id":"2411.17571","title":"Uncertainty quantification for White Matter Hyperintensity segmentation detects silent failures and improves automated Fazekas quantification","created":"2024-11-26","cited_by_count":0,"area":null},{"id":"2503.08970","title":"Evaluation of state-of-the-art deep learning models in the segmentation of the heart ventricles in parasternal short-axis echocardiograms","created":"2025-03-12","cited_by_count":0,"area":null},{"id":"2510.07126","title":"Validation of Various Normalization Methods for Brain Tumor Segmentation: Can Federated Learning Overcome This Heterogeneity?","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2607.22749","title":"Post-Operative Glioma Segmentation via Loss Stabilization, Normalization and Subspace Attention","created":"2026-07-23","cited_by_count":0,"area":null},{"id":"2608.03612","title":"Predictive Enhancement Calibration for Latent Breast MRI Virtual Contrast Enhancement","created":"2026-08-04","cited_by_count":0,"area":null},{"id":"2609.36837","title":"You Cannot Recover What Was Never Measured: Quantifying the Information Ceiling of Ultra-Low-Field MRI Super-Resolution","created":"2026-09-29","cited_by_count":0,"area":null}],"next_cursor":null}