{"total":16,"rows":[{"id":"2104.10029","title":"Multiple Sclerosis Lesion Analysis in Brain Magnetic Resonance Images: Techniques and Clinical Applications","created":"2021-04-20","cited_by_count":10,"area":null},{"id":"2105.04881","title":"Applications of Deep Learning Techniques for Automated Multiple Sclerosis Detection Using Magnetic Resonance Imaging: A Review","created":"2021-05-11","cited_by_count":10,"area":null},{"id":"2105.10559","title":"Hyper-Convolution Networks for Biomedical Image Segmentation","created":"2021-05-21","cited_by_count":10,"area":null},{"id":"2301.07895","title":"Spatially Covariant Lesion Segmentation","created":"2023-01-19","cited_by_count":10,"area":null},{"id":"2103.07240","title":"Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs","created":"2021-03-12","cited_by_count":4,"area":null},{"id":"2106.00919","title":"Self-supervised Lesion Change Detection and Localisation in Longitudinal Multiple Sclerosis Brain Imaging","created":"2021-06-02","cited_by_count":4,"area":null},{"id":"2110.00948","title":"Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans","created":"2021-10-03","cited_by_count":2,"area":null},{"id":"2201.01832","title":"Multiple Sclerosis Lesions Segmentation using Attention-Based CNNs in FLAIR Images","created":"2022-01-05","cited_by_count":2,"area":null},{"id":"2202.02701","title":"Hyper-Convolutions via Implicit Kernels for Medical Imaging","created":"2022-02-06","cited_by_count":2,"area":null},{"id":"2508.03982","title":"UNISELF: A Unified Network with Instance Normalization and Self-Ensembled Lesion Fusion for Multiple Sclerosis Lesion Segmentation","created":"2025-08-06","cited_by_count":2,"area":null},{"id":"2101.06772","title":"Latent Space Analysis of VAE and Intro-VAE applied to 3-dimensional MR Brain Volumes of Multiple Sclerosis, Leukoencephalopathy, and Healthy Patients","created":"2021-01-17","cited_by_count":1,"area":null},{"id":"2108.11791","title":"Ensemble CNN and Uncertainty Modeling to Improve Automatic Identification/Segmentation of Multiple Sclerosis Lesions in Magnetic Resonance Imaging","created":"2021-08-26","cited_by_count":0,"area":null},{"id":"2302.09516","title":"A Bibliography of Multiple Sclerosis Lesions Detection Methods using Brain MRIs","created":"2023-02-19","cited_by_count":0,"area":null},{"id":"2410.02129","title":"DMC-Net: Lightweight Dynamic Multi-Scale and Multi-Resolution Convolution Network for Pancreas Segmentation in CT Images","created":"2024-10-03","cited_by_count":0,"area":null},{"id":"2510.07447","title":"VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics","created":"2025-10-08","cited_by_count":0,"area":null},{"id":"2605.09666","title":"Rethinking Evaluation of Multiple Sclerosis (MS) Lesion Segmentation Models","created":"2026-05-10","cited_by_count":0,"area":null}],"next_cursor":null}