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
Latest papers with no code
Is Two-shot All You Need? A Label-efficient Approach for Video Segmentation in Breast Ultrasound
Breast lesion segmentation from breast ultrasound (BUS) videos could assist in early diagnosis and treatment.
Vision Transformer-based Multimodal Feature Fusion Network for Lymphoma Segmentation on PET/CT Images
Methods: Our lymphoma segmentation approach combines a vision transformer with dual encoders, adeptly fusing PET and CT data via multimodal cross-attention fusion (MMCAF) module.
Slicer Networks
In medical imaging, scans often reveal objects with varied contrasts but consistent internal intensities or textures.
Local Gamma Augmentation for Ischemic Stroke Lesion Segmentation on MRI
Data augmentation techniques can compensate for a lack of training samples.
Transformer-CNN Fused Architecture for Enhanced Skin Lesion Segmentation
The segmentation of medical images is important for the improvement and creation of healthcare systems, particularly for early disease detection and treatment planning.
SLP-Net:An efficient lightweight network for segmentation of skin lesions
Prompt treatment for melanoma is crucial.
Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net
The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, which are often time-consuming and require a high level of expertise.
A Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data
In this paper, we propose a unified framework for kidney cancer diagnosis with incomplete multi-phase CT, which simultaneously recovers missing CT images and classifies cancer subtypes using the completed set of images.
Towards an accurate and generalizable multiple sclerosis lesion segmentation model using self-ensembled lesion fusion
Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast magnetic resonance (MR) images provides improved efficiency and reproducibility compared to manual delineation.
Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation
We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs.
