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NeurologicalAlzheimer'sModel
+studies+have+shown+that+Alzheimer%27s+Disease+(AD)+induces+both+localised+and+widespread+neural+degenerative+changes+throughout+the+brain.+However%252C+the+absence+of+segmentation+that+highlights+brain+degenerative+changes+presents+unique+challenges+for+training+CNN-based+classifiers+in+a+supervised+fashion.+In+this+work%252C+we+evaluated+several+unsupervised+methods+to+train+a+feature+extractor+for+downstream+AD+vs.+CN+classification.+Using+the+3D+T1-weighted+MRI+data+of+cognitive+normal+(CN)+subjects+from+the+synthetic+neuroimaging+LDM100K+dataset%252C+lightweight+3D+CNN-based+models+are+trained+for+brain+age+prediction%252C+brain+image+rotation+classification%252C+brain+image+reconstruction+and+a+multi-head+task+combining+all+three+tasks+into+one.+Feature+extractors+trained+on+the+LDM100K+synthetic+dataset+achieved+similar+performance+compared+to+the+same+model+using+real-world+data.+This+supports+the+feasibility+of+utilising+large-scale+synthetic+data+for+pretext+task+training.+All+the+training+and+testing+splits+are+performed+on+the+subject-level+to+prevent+data+leakage+issues.+Alongside+the+simple+preprocessing+steps%252C+the+random+cropping+data+augmentation+technique+shows+consistent+improvement+across+all+experiments.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.a88c3fbe5543faae9d2ee31dbbe2e8b7%3frik%3dCkwD4jSDElU38A%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Neurological+Alzheimer%27s+Model&simid=608011157990677709&FORM=IRPRST&ck=D76E756324C2F3E805E759C2F315BC94&selectedIndex=25&itb=0)
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