India Shelley, Debanjan Haldar, Keenan Piper, Michael Baldassarri, Adam Leibold, Kevin Hines, Maikerly Reyes, Justin Williams, Christopher Farrell, Aria Mahtabfar
Introducing a novel hybrid educational boot camp to augment medical student training in neurosurgery
Journal of neurosurgery
deep learning nowadays offers expertlevel sometimes even superexpertlevel performance achieving performance demands massive annotated data training eg googles proprietary cxr foundation model cxrfm trained 821544 labeled mostly private chest xrays cxrs numerous datasets publicly available medical imaging individually small heterogeneous expert labels envision powerful robust foundation model trained aggregating numerous small public datasets realize vision developed ark framework accrues reuses knowledge heterogeneous expert annotations various datasets proof concept trained two ark models 335484 704363 cxrs respectively merging several datasets including chestxray14 chexpert mimicii vindrcxr evaluated wide range imaging tasks covering classification segmentation via finetuning linearprobing genderbias analysis demonstrated arks superior robust performance stateoftheart sota fullyselfsupervised baselines googles proprietary cxrfm enhanced performance attributed simple yet powerful observation aggregating numerous public datasets diversifies patient populations accrues knowledge diverse experts yielding unprecedented performance yet saving annotation cost codes pretrained models released githubcomjlianglabark hope ark exerts important impact open science accruing reusing knowledge expert annotations public datasets potentially surpass performance proprietary models trained unusually large data inspiring many researchers worldwide share codes datasets build open foundation models accelerate open science democratize deep learning medical imaging