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홈뉴스연구 논문튜토리얼데이터셋백과사전SOTALLM 모델GPU 랭킹컨퍼런스
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한국어
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  4. Medical Image Segmentation On Bkai Igh

Medical Image Segmentation On Bkai Igh

평가 지표

Average Dice
mIoU

평가 결과

이 벤치마크에서 각 모델의 성능 결과

모델 이름
Average Dice
mIoU
Paper TitleRepository
TGANet0.90230.8409TGANet: Text-guided attention for improved polyp segmentation
ColonSegNet0.6881-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
BlazeNeo0.78802-BlazeNeo: Blazing fast polyp segmentation and neoplasm detection
FocalUNet0.8021-Focal-UNet: UNet-like Focal Modulation for Medical Image Segmentation
NeoUNet0.80723-NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection-
EMCAD0.9296-EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation
TransResU-Net0.91540.8568TransResU-Net: Transformer based ResU-Net for Real-Time Colonoscopy Polyp Segmentation
RaBiT0.940.886RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation
0 of 9 row(s) selected.
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한국어

소개

회사 소개데이터셋 도움말

제품

뉴스튜토리얼데이터셋백과사전

링크

TVM 한국어Apache TVMOpenBayes

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