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Is it possible to develop an "AI Pathologist" to pass the board-certified examination of the American Board of Pathology (ABP)? To build such a system, three challenges need to be addressed.
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Liunian Harold Li, Mark Yatskar, Da Yin, Cho-Jui Hsieh, and Kai-Wei Chang · 1908
Earlier work this paper cites.
Text-book of pathology
Robert Muir et al · 1941
Earlier work this paper cites.
Basic pathology
Stanley L Robbins, Marcia Angell, and Vinay Kumar · 1981
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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