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Lesion Segmentation

Lesion segmentation is the task of segmenting out lesions from other objects in medical based images.

( Image credit: D-UNet )

Papers

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Showing 251–275 of 640 papers

TitleStatusHype
Deep Learning based Novel Cascaded Approach for Skin Lesion Analysis—0
GraVIS: Grouping Augmented Views from Independent Sources for Dermatology Analysis—0
Does image resolution impact chest X-ray based fine-grained Tuberculosis-consistent lesion segmentation?—0
DRG-Net: Interactive Joint Learning of Multi-lesion Segmentation and Classification for Diabetic Retinopathy Grading—0
DAE-Former: Dual Attention-guided Efficient Transformer for Medical Image SegmentationCode1
DuAT: Dual-Aggregation Transformer Network for Medical Image SegmentationCode1
HACA3: A Unified Approach for Multi-site MR Image Harmonization—0
Boundary-rendering network for breast lesion segmentation in ultrasound images—0
Investigating certain choices of CNN configurations for brain lesion segmentation—0
Weakly-supervised detection of AMD-related lesions in color fundus images using explainable deep learning—0
DSNet: a simple yet efficient network with dual-stream attention for lesion segmentation—0
Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification—0
MAPPING: Model Average with Post-processing for Stroke Lesion SegmentationCode1
ESKNet-An enhanced adaptive selection kernel convolution for breast tumors segmentationCode1
MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion SegmentationCode1
Attention Swin U-Net: Cross-Contextual Attention Mechanism for Skin Lesion SegmentationCode1
Automated analysis of diabetic retinopathy using vessel segmentation maps as inductive bias—0
U-Net-based Models for Skin Lesion Segmentation: More Attention and AugmentationCode1
Segmentation of Multiple Sclerosis Lesions across Hospitals: Learn Continually or Train from Scratch?Code0
UNet-2022: Exploring Dynamics in Non-isomorphic Architecture—0
Bag of Tricks for Developing Diabetic Retinopathy Analysis Framework to Overcome Data Scarcity—0
MKIS-Net: A Light-Weight Multi-Kernel Network for Medical Image Segmentation—0
Whole-body tumor segmentation of 18F -FDG PET/CT using a cascaded and ensembled convolutional neural networks—0
Improved automated lesion segmentation in whole-body FDG/PET-CT via Test-Time AugmentationCode0
Exploring Vanilla U-Net for Lesion Segmentation from Whole-body FDG-PET/CT ScansCode1
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