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医学图像分割
医学图像分割是计算机视觉领域的一项任务,旨在将医学图像划分为多个区域,每个区域代表图像中不同的感兴趣对象或结构。其目标是提供这些对象的精确和准确表示,主要用于诊断、治疗规划和定量分析。
Kvasir-SEG
SSFormer-L
CVC-ClinicDB
DUCK-Net
ETIS-LARIBPOLYPDB
DUCK-Net
CVC-ColonDB
RAPUNet
Synapse multi-organ CT
Interactive AI-SAM gt box
Automatic Cardiac Diagnosis Challenge (ACDC)
FCT
MoNuSeg
Stardist
GlaS
Hi-gMISnet
BKAI-IGH NeoPolyp-Small
QTSeg
2018 Data Science Bowl
DoubleUNet
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
MERIT
ACDC
FCT
ISIC 2018
ProMISe
DRIVE
Medical Segmentation Decathlon
Swin UNETR
CVC-VideoClinicDB
ResUNet++ + TTA
Kvasir-Instrument
DoubleUNet
EM
UNet++
Brain US
MedT
ISBI 2012 EM Segmentation
CE-Net
CHASE_DB1
RITE
KiU-Net
ISIC2018
EMCAD
LiTS2017
UNet 3+
ROBUST-MIS
KvasirCapsule-SEG
NanoNet
Medico automatic polyp segmentation challenge (dataset)
ISIC 2018
EMCAD
Endotect Polyp Segmentation Challenge Dataset
DDANet
ENSeg
YOLOv8-m + SAM-b
CHAOS MRI Dataset
MS-Dual-Guided
ASU-Mayo Clinic dataset
ResUNet++
MoNuSeg 2018
MosMedData
C2FVL
SegPC-2021
DCSAU-Net
Hyper-Kvasir Dataset
efficientnetb1
Autoimmune Dataset
Unet with APP
MoNuSAC
MaxViT-UNet
Extended Task10_Colon Medical Decathlon
nnUNet
Autooral dataset
HF-UNet
2015 MICCAI Polyp Detection
DoubleUNet
iSEG 2017 Challenge
HyperDenseNet
PROMISE12
Hi-gMISnet
Synapse
nnFormer
MICCAI 2015 Head and Neck Challenge
AnatomyNet
Cell
HSVM
MS-Dual-Guided
AMOS
MedNeXt-L (5x5x5)