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
2018 Data Science Bowl
DoubleUNet
BKAI-IGH NeoPolyp-Small
QTSeg
MICCAI 2015 Multi-Atlas Abdomen Labeling Challenge
MERIT
ACDC
FCT
CVC-VideoClinicDB
ResUNet++ + TTA
DRIVE
ISIC 2018
ProMISe
Medical Segmentation Decathlon
Swin UNETR
Brain US
MedT
CHASE_DB1
EM
UNet++
ISBI 2012 EM Segmentation
CE-Net
ISIC2018
EMCAD
Kvasir-Instrument
DoubleUNet
RITE
KiU-Net
Endotect Polyp Segmentation Challenge Dataset
DDANet
ISIC 2018
EMCAD
KvasirCapsule-SEG
NanoNet
LiTS2017
UNet 3+
Medico automatic polyp segmentation challenge (dataset)
ROBUST-MIS
2015 MICCAI Polyp Detection
DoubleUNet
AMOS
MedNeXt-L (5x5x5)
ASU-Mayo Clinic dataset
ResUNet++
Autoimmune Dataset
Unet with APP
Autooral dataset
HF-UNet
Cell
CHAOS MRI Dataset
MS-Dual-Guided
ENSeg
YOLOv8-m + SAM-b
Extended Task10_Colon Medical Decathlon
nnUNet
HSVM
MS-Dual-Guided
Hyper-Kvasir Dataset
efficientnetb1
iSEG 2017 Challenge
HyperDenseNet
MICCAI 2015 Head and Neck Challenge
AnatomyNet
MoNuSAC
MaxViT-UNet
MoNuSeg 2018
MosMedData
C2FVL
PROMISE12
Hi-gMISnet
SegPC-2021
DCSAU-Net
Synapse
nnFormer