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+has+emerged+as+a+key+technique+for+training+networks+that+can+generalize+well+to+diverse+tasks+without+task-specific+supervision.+This+property+makes+SSL+desirable+for+computational+pathology%252C+the+study+of+digitized+images+of+tissues%252C+as+there+are+many+target+applications+and+often+limited+labeled+training+samples.+However%252C+SSL+algorithms+and+models+have+been+primarily+developed+in+the+field+of+natural+images+and+whether+their+performance+can+be+improved+by+adaptation+to+particular+domains+remains+an+open+question.+In+this+work%252C+we+present+an+investigation+of+modifications+to+SSL+for+pathology+data%252C+specifically+focusing+on+the+DINOv2+algorithm.+We+propose+alternative+augmentations%252C+regularization+functions%252C+and+position+encodings+motivated+by+the+characteristics+of+pathology+images.+We+evaluate+the+impact+of+these+changes+on+several+benchmarks+to+demonstrate+the+value+of+tailored+approaches.&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.068c556e3e09f47f07826106d7130bcb%3frik%3dwjCr%252fk29xYV2ew%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Self+Supervised+Learning+Abnormal+Detection&FORM=IRPRST&ck=AAF20E989AFD095594F9EE7FC0AF2FC3&selectedIndex=9&itb=0)
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