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MedSegBench: A Comprehensive Benchmark for Medical Image Segmentation in Diverse Data Modalities
2024-08-28
health informatics
Title + abstract only
View on medRxiv
Show abstract
MedSegBench is a comprehensive benchmark designed to evaluate deep learning models for medical image segmentation across a wide range of modalities.. This benchmark includes 35 datasets with over 60,000 images, covering modalities such as ultrasound, MRI, and X-ray. It addresses challenges in medical imaging, such as variability in image quality and dataset imbalances, by providing standardized datasets with train/validation/test splits. The benchmark supports binary and multi-class segmentation...
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