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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 21–30 of 786 papers

TitleStatusHype
PhaseGen: A Diffusion-Based Approach for Complex-Valued MRI Data GenerationCode1
GBT-SAM: Adapting a Foundational Deep Learning Model for Generalizable Brain Tumor Segmentation via Efficient Integration of Multi-Parametric MRI DataCode1
A Reverse Mamba Attention Network for Pathological Liver SegmentationCode1
Lung-DDPM: Semantic Layout-guided Diffusion Models for Thoracic CT Image SynthesisCode1
Triad: Vision Foundation Model for 3D Magnetic Resonance ImagingCode1
CLISC: Bridging clip and sam by enhanced cam for unsupervised brain tumor segmentationCode1
CSC-PA: Cross-image Semantic Correlation via Prototype Attentions for Single-network Semi-supervised Breast Tumor SegmentationCode1
XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-DecoderCode1
cWDM: Conditional Wavelet Diffusion Models for Cross-Modality 3D Medical Image SynthesisCode1
Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and PediatricsCode1
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