Tumor Segmentation
227 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
Preoperative brain tumor imaging: models and software for segmentation and standardized reporting
Tumor segmentation models were trained using the AGU-Net architecture with different preprocessing steps and protocols.
3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers
In this paper, we extend the 2D TransUNet architecture to a 3D network by building upon the state-of-the-art nnU-Net architecture, and fully exploring Transformers' potential in both the encoder and decoder design.
SegAN: Adversarial Network with Multi-scale $L_1$ Loss for Medical Image Segmentation
Extensive experimental results demonstrate the effectiveness of the proposed SegAN with multi-scale loss: on BRATS 2013 SegAN gives performance comparable to the state-of-the-art for whole tumor and tumor core segmentation while achieves better precision and sensitivity for Gd-enhance tumor core segmentation; on BRATS 2015 SegAN achieves better performance than the state-of-the-art in both dice score and precision.
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation from CT Volumes
Our method outperformed other state-of-the-arts on the segmentation results of tumors and achieved very competitive performance for liver segmentation even with a single model.
3D RoI-aware U-Net for Accurate and Efficient Colorectal Tumor Segmentation
With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detailpreserving segmentation and therefore enlarged applicable volume size and effective receptive field.
Lesion Focused Super-Resolution
GinZhu/RDST
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Super-resolution (SR) for image enhancement has great importance in medical image applications.
3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction
Dice coefficients for enhancing tumor, tumor core, and the whole tumor are 0. 737, 0. 807 and 0. 894 respectively on the validation dataset.
MIScnn: A Framework for Medical Image Segmentation with Convolutional Neural Networks and Deep Learning
The increased availability and usage of modern medical imaging induced a strong need for automatic medical image segmentation.
A Generalized Deep Learning Framework for Whole-Slide Image Segmentation and Analysis
However, the size of images and variability in histopathology tasks makes it a challenge to develop an integrated framework for histopathology image analysis.
Multi-level colonoscopy malignant tissue detection with adversarial CAC-UNet
bupt-ai-cz/CAC-UNet-DigestPath2019
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The automatic and objective medical diagnostic model can be valuable to achieve early cancer detection, and thus reducing the mortality rate.
