Tumor Segmentation
223 papers with code • 3 benchmarks • 9 datasets
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.
Benchmarks
These leaderboards are used to track progress in Tumor Segmentation
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Libraries
Use these libraries to find Tumor Segmentation models and implementations
BraTS 2015
LiTS17
DigestPath
BraTS 2016
BraTS 2014
CSAW-S
NSCLC-Radiogenomics
CoCaHis
Latest papers
MAProtoNet: A Multi-scale Attentive Interpretable Prototypical Part Network for 3D Magnetic Resonance Imaging Brain Tumor Classification
Specifically, we introduce a concise multi-scale module to merge attentive features from quadruplet attention layers, and produces attribution maps.
Federated Modality-specific Encoders and Multimodal Anchors for Personalized Brain Tumor Segmentation
qdaiing/fedmema
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In practice, it is not uncommon that some FL participants only possess a subset of the complete imaging modalities, posing inter-modal heterogeneity as a challenge to effectively training a global model on all participants' data.
D-Net: Dynamic Large Kernel with Dynamic Feature Fusion for Volumetric Medical Image Segmentation
sotiraslab/dlk
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D-Net is able to effectively utilize a multi-scale large receptive field and adaptively harness global contextual information.
Attention-Enhanced Hybrid Feature Aggregation Network for 3D Brain Tumor Segmentation
In our approach, we utilized a multi-scale, attention-guided and hybrid U-Net-shaped model -- GLIMS -- to perform 3D brain tumor segmentation in three regions: Enhancing Tumor (ET), Tumor Core (TC), and Whole Tumor (WT).
Re-DiffiNet: Modeling discrepancies in tumor segmentation using diffusion models
Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons.
SEDNet: Shallow Encoder-Decoder Network for Brain Tumor Segmentation
chollette/SEDNet_Shallow-Encoder-Decoder-Network-for-Brain-Tumor-Segmentation
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Despite the advancement in computational modeling towards brain tumor segmentation, of which several models have been developed, it is evident from the computational complexity of existing models which are still at an all-time high, that performance and efficiency under clinical application scenarios are limited.
CT Liver Segmentation via PVT-based Encoding and Refined Decoding
Accurate liver segmentation from CT scans is essential for effective diagnosis and treatment planning.
To deform or not: treatment-aware longitudinal registration for breast DCE-MRI during neoadjuvant chemotherapy via unsupervised keypoints detection
fiy2w/treatment-aware-longitudinal-registration
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We use a clinical dataset with 1630 MRI scans from 314 patients treated with NAC.
Training and Comparison of nnU-Net and DeepMedic Methods for Autosegmentation of Pediatric Brain Tumors
External validation of the trained nnU-Net model on the multi-institutional BraTS-PEDs 2023 dataset revealed high generalization capability in segmentation of whole tumor and tumor core with Dice scores of 0. 87+/-0. 13 (0. 91) and 0. 83+/-0. 18 (0. 89), respectively.
Beyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
datct00/Beyond-Traditional-Approaches-Multi-Task-Network-for-Breast-Ultrasound-Diagnosis
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Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective.
