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Medical Image Segmentation On Bkai Igh

Metriken

Average Dice

mIoU

Ergebnisse

Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

Modellname	Average Dice	mIoU	Paper Title	Repository
TGANet	0.9023	0.8409	TGANet: Text-guided attention for improved polyp segmentation
ColonSegNet	0.6881	-	Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
QTSeg	-	-	QTSeg: A Query Token-Based Dual-Mix Attention Framework with Multi-Level Feature Distribution for Medical Image Segmentation
BlazeNeo	0.78802	-	BlazeNeo: Blazing fast polyp segmentation and neoplasm detection
FocalUNet	0.8021	-	Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation
NeoUNet	0.80723	-	NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection	-
EMCAD	0.9296	-	EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
TransResU-Net	0.9154	0.8568	TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
RaBiT	0.94	0.886	RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation

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Medical Image Segmentation On Bkai Igh | SOTA | HyperAI