Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study

Y Sadegheih, D Merhof - International Workshop on PRedictive …, 2024 - Springer
Brain metastasis segmentation poses a significant challenge in medical imaging due to the
complex presentation and variability in size and location of metastases. In this study, we first …

[HTML][HTML] Transfer Learning Approaches for Brain Metastases Screenings

MSK Luu, BN Tuchinov, V Suvorov, RM Kenzhin… - Biomedicines, 2024 - mdpi.com
Background: In this study, we examined the effectiveness of transfer learning in improving
automatic segmentation of brain metastases on magnetic resonance imaging scans, with …

FANCL: Feature-Guided Attention Network with Curriculum Learning for Brain Metastases Segmentation

Z Liu, X Liu, L Qu, Y Shi - arXiv preprint arXiv:2410.22057, 2024 - arxiv.org
Accurate segmentation of brain metastases (BMs) in MR image is crucial for the diagnosis
and follow-up of patients. Methods based on deep convolutional neural networks (CNNs) …

A Flexible 2.5 D Medical Image Segmentation Approach with In-Slice and Cross-Slice Attention

A Kumar, H Jiang, M Imran, C Valdes, G Leon… - arXiv preprint arXiv …, 2024 - arxiv.org
Deep learning has become the de facto method for medical image segmentation, with 3D
segmentation models excelling in capturing complex 3D structures and 2D models offering …

in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study

Y Sadegheih¹, D Merhof - … , PRIME 2024, Held in Conjunction with …, 2025 - books.google.com
Brain metastasis segmentation poses a significant challenge in medical imaging due to the
complex presentation and variability in size and location of metastases. In this study, we first …