Transition　from　conventional　to　digital　pathology　requires　a　new　category　of　biomedical　informatic　infrastructure　which　could　facilitate　delicate　pathological　routine.　Pathological　diagnoses　are　sensitive　to　many　external　factors　and　is　known　to　be　subjective.　Only　systems　that　can　meet　strict　requirements　in　pathology　would　be　able　to　run　along　pathological　routines　and　eventually　digitized　the　area,　and　the　developed　platform　should　comply　with　existing　pathological　routines　and　international　standards.　Currently,　there　are　a　number　of　available　software　tools　which　can　perform　histopathological　tasks　including　virtual　slide　viewing,　annotating,　and　basic　image　analysis,　however,　none　of　them　can　serve　as　a　digital　platform　for　pathology.　Here　we　describe　OpenHI2,　an　enhanced　version　Open　Histopathological　Image　platform　which　is　capable　of　supporting　all　basic　pathological　tasks　and　file　formats;　ready　to　be　deployed　in　medical　institutions　on　a　standard　server　environment　or　cloud　computing　infrastructure.　In　this　paper,　we　also　describe　the　development　decisions　for　the　platform　and　propose　solutions　to　overcome　technical　challenges　including　responsive　region　retrieval　and　viewing,　virtual　slide　magnification,　recording　of　diagnostic　areas.　These　factors　would　promote　OpenHI2　be　used　as　a　platform　for　histopathological　images　in　real-world　clinical　settings.　Furthermore,　in　research,　OpenHI2　inherited　the　annotation　functionality　from　the　previous　version,　thus　acquired　annotations　can　be　directly　utilized　by　the　newly　added　machine　learning　module　which　include　popular　machine　learning　models　to　perform　tasks　such　as　histology　image　classification　and　segmentation　in　the　same　environment.　Addition　can　be　made　to　the　platform　since　each　component　is　modularized　and　fully　documented.　OpenHI2　is　free,　open-source,　and　available　at　https://gitlab.com/BioAI/OpenHI.　©　2019　IEEE.