{"version":"1.0","provider_name":"Medical Segmentation with Foundation Models: A Prompt-Based, Text-Guided, Training-Free Pipeline","provider_url":"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1","title":"Experiments - Medical Segmentation with Foundation Models: A Prompt-Based, Text-Guided, Training-Free Pipeline","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"qRa5Sw9awH\"><a href=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/experiments\/\">Experiments<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/experiments\/embed\/#?secret=qRa5Sw9awH\" width=\"600\" height=\"338\" title=\"&#8220;Experiments&#8221; &#8212; Medical Segmentation with Foundation Models: A Prompt-Based, Text-Guided, Training-Free Pipeline\" data-secret=\"qRa5Sw9awH\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","description":"Datasets The datasets we use to validate are presented in the table below. Dice Similarity Coefficient For the performance measurement, we use the Dice Similarity Coefficient. Specifically, in our case, we visualize the ground truth, mask prediction, and overlap in three different colors. The yellow part of the image below is the overlap between the &hellip; Continue reading \"\"","thumbnail_url":"https:\/\/mscvprojects.ri.cmu.edu\/2025team7-1\/wp-content\/uploads\/sites\/130\/2025\/12\/image-4.png","thumbnail_width":818,"thumbnail_height":218}