Tumor Segmentation
230 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
Most implemented papers
Adding Seemingly Uninformative Labels Helps in Low Data Regimes
Evidence suggests that networks trained on large datasets generalize well not solely because of the numerous training examples, but also class diversity which encourages learning of enriched features.
What is the best data augmentation for 3D brain tumor segmentation?
Training segmentation networks requires large annotated datasets, which in medical imaging can be hard to obtain.
NuCLS: A scalable crowdsourcing, deep learning approach and dataset for nucleus classification, localization and segmentation
High-resolution mapping of cells and tissue structures provides a foundation for developing interpretable machine-learning models for computational pathology.
TransBTS: Multimodal Brain Tumor Segmentation Using Transformer
To capture the local 3D context information, the encoder first utilizes 3D CNN to extract the volumetric spatial feature maps.
The Federated Tumor Segmentation (FeTS) Challenge
The goals of the FeTS challenge are directly represented by the two included tasks: 1) the identification of the optimal weight aggregation approach towards the training of a consensus model that has gained knowledge via federated learning from multiple geographically distinct institutions, while their data are always retained within each institution, and 2) the federated evaluation of the generalizability of brain tumor segmentation models "in the wild", i. e. on data from institutional distributions that were not part of the training datasets.
ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities
Wangyixinxin/ACN
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Specifically, ACN adopts a novel co-training network, which enables a coupled learning process for both full modality and missing modality to supplement each other's domain and feature representations, and more importantly, to recover the `missing' information of absent modalities.
Modality-aware Mutual Learning for Multi-modal Medical Image Segmentation
YaoZhang93/MAML
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To this end, we propose a novel mutual learning (ML) strategy for effective and robust multi-modal liver tumor segmentation.
DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT
mungomeng/survival-deepmts
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However, the models using the whole target regions will also include non-relevant background information, while the models using segmented tumor regions will disregard potentially prognostic information existing out of primary tumors (e. g., local lymph node metastasis and adjacent tissue invasion).
Optimized U-Net for Brain Tumor Segmentation
NVIDIA/DeepLearningExamples
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We propose an optimized U-Net architecture for a brain tumor segmentation task in the BraTS21 challenge.
Factorizer: A Scalable Interpretable Approach to Context Modeling for Medical Image Segmentation
Specifically, we propose a linearly scalable approach to context modeling, formulating Nonnegative Matrix Factorization (NMF) as a differentiable layer integrated into a U-shaped architecture.
