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Tumor Segmentation

Tumor Segmentation is the task of identifying the spatial location of a tumor. It is a pixel-level prediction where each pixel is classified as a tumor or background. The most popular benchmark for this task is the BraTS dataset. The models are typically evaluated with the Dice Score metric.

Papers

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Showing 371–380 of 786 papers

TitleStatusHype
MRI brain tumor segmentation using informative feature vectors and kernel dictionary learning—0
RT-SRTS: Angle-Agnostic Real-Time Simultaneous 3D Reconstruction and Tumor Segmentation from Single X-Ray ProjectionCode0
Synthesizing Missing MRI Sequences from Available Modalities using Generative Adversarial Networks in BraTS Dataset—0
Empirical Evaluation of the Segment Anything Model (SAM) for Brain Tumor Segmentation—0
Automated 3D Segmentation of Kidneys and Tumors in MICCAI KiTS 2023 Challenge—0
Whole Slide Multiple Instance Learning for Predicting Axillary Lymph Node MetastasisCode0
Iterative Semi-Supervised Learning for Abdominal Organs and Tumor SegmentationCode0
Generating 3D Brain Tumor Regions in MRI using Vector-Quantization Generative Adversarial Networks—0
3D-DDA: 3D Dual-Domain Attention for Brain Tumor SegmentationCode0
Exploring SAM Ablations for Enhancing Medical Segmentation in Radiology and Pathology—0
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