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
TJDR: A High-Quality Diabetic Retinopathy Pixel-Level Annotation Dataset
Diabetic retinopathy (DR), as a debilitating ocular complication, necessitates prompt intervention and treatment.
MCANet: Medical Image Segmentation with Multi-Scale Cross-Axis Attention
To process the significant variations of lesion regions or organs in individual sizes and shapes, we also use multiple convolutions of strip-shape kernels with different kernel sizes in each axial attention path to improve the efficiency of the proposed MCA in encoding spatial information.
U-Net v2: Rethinking the Skip Connections of U-Net for Medical Image Segmentation
We evaluate our method on several public medical image segmentation datasets for skin lesion segmentation and polyp segmentation, and the experimental results demonstrate the segmentation accuracy of our new method over state-of-the-art methods, while preserving memory and computational efficiency.
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion Segmentation
alikm6/DEU-Net
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IEEE Access 2023
The computer-aided diagnosis (CAD) of skin diseases relies heavily on automated skin lesion segmentation, albeit presenting considerable challenges due to lesion diversity in shape, size, color, and texture, as well as potential blurry boundaries with surrounding tissues.
Only Positive Cases: 5-fold High-order Attention Interaction Model for Skin Segmentation Derived Classification
In this paper, we propose a multiple high-order attention interaction model (MHA-UNet) for use in a highly explainable skin lesion segmentation task.
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
Our method outperforms classical intensity augmentations as well as the intensity augmentation pipeline of the popular nn-UNet on multiple datasets.
Fuzzy Information Seeded Region Growing for Automated Lesions After Stroke Segmentation in MR Brain Images
Designed to effectively delineate the complex and irregular boundaries of stroke lesions, the FISRG algorithm combines fuzzy logic with Seeded Region Growing (SRG) techniques, aiming to enhance segmentation accuracy.
Morphology-Enhanced CAM-Guided SAM for weakly supervised Breast Lesion Segmentation
This innovative framework is specifically designed for weakly supervised lesion segmentation in early-stage breast ultrasound images.
Comprehensive Evaluation and Insights into the Use of Deep Neural Networks to Detect and Quantify Lymphoma Lesions in PET/CT Images
microsoft/lymphoma-segmentation-dnn
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This study performs comprehensive evaluation of four neural network architectures (UNet, SegResNet, DynUNet, and SwinUNETR) for lymphoma lesion segmentation from PET/CT images.
Structural-Based Uncertainty in Deep Learning Across Anatomical Scales: Analysis in White Matter Lesion Segmentation
The results from a multi-centric MRI dataset of 172 patients demonstrate that our proposed measures more effectively capture model errors at the lesion and patient scales compared to measures that average voxel-scale uncertainty values.
