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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 451–475 of 786 papers

TitleStatusHype
Automatic Segmentation of Vestibular Schwannoma from T2-Weighted MRI by Deep Spatial Attention with Hardness-Weighted Loss—0
Topology-Aware Focal Loss for 3D Image Segmentation—0
Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI—0
Automatic segmentation of kidney and liver tumors in CT images—0
Lung-Originated Tumor Segmentation from Computed Tomography Scan (LOTUS) Benchmark—0
Lung tumor segmentation in MRI mice scans using 3D nnU-Net with minimum annotations—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Towards a Multimodal MRI-Based Foundation Model for Multi-Level Feature Exploration in Segmentation, Molecular Subtyping, and Grading of Glioma—0
MAG-Net: Multi-task attention guided network for brain tumor segmentation and classification—0
Automatic Liver Lesion Segmentation Using A Deep Convolutional Neural Network Method—0
Weakly supervised pan-cancer segmentation tool—0
Automatic Liver Lesion Detection using Cascaded Deep Residual Networks—0
MAST-Pro: Dynamic Mixture-of-Experts for Adaptive Segmentation of Pan-Tumors with Knowledge-Driven Prompts—0
MBA-Net: SAM-driven Bidirectional Aggregation Network for Ovarian Tumor Segmentation—0
Deep segmentation networks predict survival of non-small cell lung cancer—0
MDNet: Multi-Decoder Network for Abdominal CT Organs Segmentation—0
Med-DANet: Dynamic Architecture Network for Efficient Medical Volumetric Segmentation—0
Towards SAMBA: Segment Anything Model for Brain Tumor Segmentation in Sub-Sharan African Populations—0
Medical Image Analysis using Deep Relational Learning—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks—0
Medical Transformer: Universal Brain Encoder for 3D MRI Analysis—0
MedMAP: Promoting Incomplete Multi-modal Brain Tumor Segmentation with Alignment—0
Automatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation—0
Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation—0
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