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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 776–786 of 786 papers

TitleStatusHype
A Weakly Supervised and Globally Explainable Learning Framework for Brain Tumor SegmentationCode0
Optimizing Synthetic Data for Enhanced Pancreatic Tumor SegmentationCode0
A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor SegmentationCode0
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided RadiotherapyCode0
Semi-Supervised Variational Autoencoder for Survival PredictionCode0
An Optimization Framework for Processing and Transfer Learning for the Brain Tumor SegmentationCode0
Parameter-efficient Fine-tuning for improved Convolutional Baseline for Brain Tumor Segmentation in Sub-Saharan Africa Adult Glioma DatasetCode0
Distributionally Robust Deep Learning using Hardness Weighted SamplingCode0
Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative StudyCode0
Simulating Dynamic Tumor Contrast Enhancement in Breast MRI using Conditional Generative Adversarial NetworksCode0
The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical OutcomesCode0
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