[CVPR 2023] Label-Free Liver Tumor Segmentation
This project compares the performance of UNet, ResUNet, SegResNet, and UNETR architectures on the 2017 LiTS dataset for liver tumor segmentation. We evaluate segmentation accuracy using the DICE score to identify key factors for effective tumor segmentation.
Deep learning project for liver and tumor segmentation using the Medical Segmentation Decathlon dataset, featuring preprocessing, model training, and evaluation of segmentation performance.
CAPPYv1 paper overview: 3D tumor copy-paste augmentation for liver tumor segmentation
LiTS-17 liver tumor localization & segmentation: EDA, spatial forensics, patient-aware splits, 2-stage ROI pipeline, Mark 1->4E checkpoint fusion, and 3D-IRCADb external validation
2.5D U-Net liver-tumour segmentation with patient-level evaluation, ensembling, and compute-performance analysis.
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