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+training+framework+called+SurgDepth%252C+and+show+state-of-the-art+(SOTA)+results+on+all+publicly+available+datasets+applicable+for+this+task.+Unlike+previous+approaches%252C+which+either+fine-tune+SOTA+segmentation+models+trained+on+natural+images%252C+or+encode+RGB+or+RGB-D+information+using+RGB+only+pre-trained+backbones%252C+SurgDepth%252C+which+is+built+on+top+of+Vision+Transformers+(ViTs)%252C+is+designed+to+encode+both+RGB+and+depth+information+through+a+simple+fusion+mechanism.+We+conduct+extensive+experiments+on+benchmark+datasets+including+EndoVis2022%252C+AutoLapro%252C+LapI2I+and+EndoVis2017+to+verify+the+efficacy+of+SurgDepth.+Specifically%252C+SurgDepth+achieves+a+new+SOTA+IoU+of+0.86+on+EndoVis+2022+SAR-RARP50+challenge+and+outperforms+the+current+best+method+by+at+least+4%2525%252C+using+a+shallow+and+compute+efficient+decoder+consisting+of+ConvNeXt+blocks.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.4315ce30f5a9c288aebe62dfc5300744%3frik%3dP3kdPVB1XM7dvw%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=RadPath+Fusion+to+Drive+Deep+Learning+Segmentation&FORM=IRPRST&ck=C3561AE75A4BF24C2C90BD839D4DDAFC&selectedIndex=14&itb=0)
