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
Synthetic Data for Robust Stroke Segmentation
Deep learning-based semantic segmentation in neuroimaging currently requires high-resolution scans and extensive annotated datasets, posing significant barriers to clinical applicability.
UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation
wurenkai/UltraLight-VM-UNet
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In this paper, we deeply explore the key elements of parameter influence in Mamba and propose an UltraLight Vision Mamba UNet (UltraLight VM-UNet) based on this.
A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge
tabrisrei/isles22_ensemble
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We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge.
LeFusion: Synthesizing Myocardial Pathology on Cardiac MRI via Lesion-Focus Diffusion Models
By redesigning the diffusion learning objectives to concentrate on lesion areas, it simplifies the model learning process and enhance the controllability of the synthetic output, while preserving background by integrating forward-diffused background contexts into the reverse diffusion process.
H-vmunet: High-order Vision Mamba UNet for Medical Image Segmentation
In the field of medical image segmentation, variant models based on Convolutional Neural Networks (CNNs) and Visual Transformers (ViTs) as the base modules have been very widely developed and applied.
ProMISe: Promptable Medical Image Segmentation using SAM
Our experiments demonstrate that such adaptive prompts significantly improve SAM's non-fine-tuned performance in MIS.
Vivim: a Video Vision Mamba for Medical Video Object Segmentation
Traditional convolutional neural networks have a limited receptive field while transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity.
Inconsistency Masks: Removing the Uncertainty from Input-Pseudo-Label Pairs
michaelvorndran/inconsistencymasks
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Efficiently generating sufficient labeled data remains a major bottleneck in deep learning, particularly for image segmentation tasks where labeling requires significant time and effort.
Development of RLK-Unet: a clinically favorable deep learning algorithm for brain metastasis detection and treatment response assessment
nibabel/RLK-Unet
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Frontiers in Oncology 2024
Methods and materials: A total of 128 patients with 1339 BMs, who underwent BM magnetic resonance imaging using the contrast-enhanced 3D T1 weighted (T1WI) turbo spin-echo black blood sequence, were included in the development of the DL algorithm.
Automated Detection of Myopic Maculopathy in MMAC 2023: Achievements in Classification, Segmentation, and Spherical Equivalent Prediction
liyihao76/MMAC_LaTIM_Solution
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As for Task 3 (prediction of spherical equivalent), we have designed a deep regression model based on the data distribution of the dataset and employed an integration strategy to enhance the model's prediction accuracy.
