Lesion Segmentation
208 papers with code • 10 benchmarks • 13 datasets
Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.
( Image credit: D-UNet )
Benchmarks
These leaderboards are used to track progress in Lesion Segmentation
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Libraries
Use these libraries to find Lesion Segmentation models and implementationsDatasets
HAM10000
DigestPath
ISIC 2018 Task 1
ISIC 2017 Task 1
FGADR
University of Waterloo skin cancer database
ISIC 2018 Task 3
Most implemented papers
Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks
In the first step, we train a FCN to segment the liver as ROI input for a second FCN.
RECOD Titans at ISIC Challenge 2017
learningtitans/isbi2017-part3
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This extended abstract describes the participation of RECOD Titans in parts 1 and 3 of the ISIC Challenge 2017 "Skin Lesion Analysis Towards Melanoma Detection" (ISBI 2017).
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
rezazad68/BCDU-Net
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In Proceedings of the IEEE/CVF international conference on computer vision workshops 2019
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
DebeshJha/2020-CBMS-DoubleU-Net
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The encouraging results, produced on various medical image segmentation datasets, show that DoubleU-Net can be used as a strong baseline for both medical image segmentation and cross-dataset evaluation testing to measure the generalizability of Deep Learning (DL) models.
Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
Automatic segmentation of the liver and its lesion is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems.
One-shot domain adaptation in multiple sclerosis lesion segmentation using convolutional neural networks
In recent years, several convolutional neural network (CNN) methods have been proposed for the automated white matter lesion segmentation of multiple sclerosis (MS) patient images, due to their superior performance compared with those of other state-of-the-art methods.
Comparative study of deep learning methods for the automatic segmentation of lung, lesion and lesion type in CT scans of COVID-19 patients
There is an increasing number of studies that propose to use deep learning to provide fast and accurate quantification of COVID-19 using chest CT scans.
CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation
HiLab-git/CA-Net
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Also, we propose a scale attention module implicitly emphasizing the most salient feature maps among multiple scales so that the CNN is adaptive to the size of an object.
Diffusion Models for Implicit Image Segmentation Ensembles
juliawolleb/diffusion-based-segmentation
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By modifying the training and sampling scheme, we show that diffusion models can perform lesion segmentation of medical images.
Efficient Multi-Scale 3D CNN with Fully Connected CRF for Accurate Brain Lesion Segmentation
We propose a dual pathway, 11-layers deep, three-dimensional Convolutional Neural Network for the challenging task of brain lesion segmentation.
