Tumor Segmentation
222 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 with no code
LATUP-Net: A Lightweight 3D Attention U-Net with Parallel Convolutions for Brain Tumor Segmentation
The proposed architecture, a Lightweight 3D ATtention U-Net with Parallel convolutions, LATUP-Net, addresses these issues.
Comparative Analysis of Image Enhancement Techniques for Brain Tumor Segmentation: Contrast, Histogram, and Hybrid Approaches
This study systematically investigates the impact of image enhancement techniques on Convolutional Neural Network (CNN)-based Brain Tumor Segmentation, focusing on Histogram Equalization (HE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and their hybrid variations.
Automated Prediction of Breast Cancer Response to Neoadjuvant Chemotherapy from DWI Data
Effective surgical planning for breast cancer hinges on accurately predicting pathological complete response (pCR) to neoadjuvant chemotherapy (NAC).
Deep Learning-Based Brain Image Segmentation for Automated Tumour Detection
Introduction: The present study on the development and evaluation of an automated brain tumor segmentation technique based on deep learning using the 3D U-Net model.
A dataset of primary nasopharyngeal carcinoma MRI with multi-modalities segmentation
Multi-modality magnetic resonance imaging data with various sequences facilitate the early diagnosis, tumor segmentation, and disease staging in the management of nasopharyngeal carcinoma (NPC).
H2ASeg: Hierarchical Adaptive Interaction and Weighting Network for Tumor Segmentation in PET/CT Images
However, modality-specific encoders used in these methods operate independently, inadequately leveraging the synergistic relationships inherent in PET and CT modalities, for example, the complementarity between semantics and structure.
3D-TransUNet for Brain Metastases Segmentation in the BraTS2023 Challenge
We identify that the Decoder-only 3D-TransUNet model should offer enhanced efficacy in the segmentation of brain metastases, as indicated by our 5-fold cross-validation on the training set.
Building Brain Tumor Segmentation Networks with User-Assisted Filter Estimation and Selection
Brain tumor image segmentation is a challenging research topic in which deep-learning models have presented the best results.
Trustworthiness of Pretrained Transformers for Lung Cancer Segmentation
We assessed the trustworthiness of two self-supervision pretrained transformer models, Swin UNETR and SMIT, for fine-tuned lung (LC) tumor segmentation using 670 CT and MRI scans.
Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks
This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors.
