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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 111–120 of 786 papers

TitleStatusHype
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial NetworksCode0
Targeted Neural Architectures in Multi-Objective Frameworks for Complete Glioma Characterization from Multimodal MRI—0
Multiscale Encoder and Omni-Dimensional Dynamic Convolution Enrichment in nnU-Net for Brain Tumor SegmentationCode1
Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET ImagingCode0
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
Spectral U-Net: Enhancing Medical Image Segmentation via Spectral Decomposition—0
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ SegmentationCode0
Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance ImagingCode0
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