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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 251–260 of 786 papers

TitleStatusHype
ONCOPILOT: A Promptable CT Foundation Model For Solid Tumor Evaluation—0
Federated brain tumor segmentation: an extensive benchmark—0
Optimizing Medical Image Segmentation with Advanced Decoder DesignCode0
Mind the Gap: Promoting Missing Modality Brain Tumor Segmentation with Alignment—0
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
Sine Wave Normalization for Deep Learning-Based Tumor Segmentation in CT/PET ImagingCode0
Cross-Organ and Cross-Scanner Adenocarcinoma Segmentation using Rein to Fine-tune Vision Foundation Models—0
multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical InformationCode0
Two Stage Segmentation of Cervical Tumors using PocketNet—0
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