Page 56 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
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Article  type  Journal  Journal  Journal  Journal  Journal  Journal


                        References  [97]  [98]   [99]     [100]     [101]       [102]




                           images  set  cases  cases  comprised  and  malignant  digital  were  Dutch  screening  and  contained  by  nodules


                           974  data  330  from  281  from  set  from  views,  training  1000  from  screening  a  that  of  were  1729  of  the  of  cancer  acquired  of  nodules
                           containing  proprietary  images  images  data  for  roughly  collected  from  full-field  cases  Permanente  facilities  non-PFNs  scans  center  lung  was  Society  (JSRT)  lung  without


                        set  set  a  US  MG  validation  mammographic  used  of  cysts  obtained  Netherlands  840  of  210  Kaiser  and  baseline  one  that  set  Japanese  Technology  confirmed  cases
                        data  data  cases,  408  digital  and  were  validation  600  The  from  9  California  PFNs  from  from  randomized  data  the  with  normal

                        on  DDSM  512  646  used  training  44,090  39,872  for  consisted  and  mammograms  in  consisted  mammograms  at  of  participants  (NELSON)  standard  from  Radiological  cases  93
                        Remark  Public  from  containing  and  was  The  of  which  4218  set  Data  masses  program  set  Data  collected  Northern  Samples  collected  eBelgian  trial  The  used  154  and  CT



              imaging.dcontinued  Modality  CNN  and  US  CNN  mammography  Mammography  Mammography  Mammography  CT  Chest  radiography









              medical   of  Type  Multitask  CNN  Deep  CNN  Deep  CNN  Deep  CNN  AlexNet



              to           and  in        lesions  solitary  breast             of
              application  detection  lesions  of  computer-aided  mammographic  discriminate  lesions  of detection  automatic  pulmonary  (PFNs)  classification




              CNN          for  description  images  for  to  tissue  soft  for  calcifications  for  of  nodules  for

              of           designed    designed  of  designed  from  designed  designed  classification  perifissural  designed  nodule
              Literature  Applications  Model  semantic  diagnostic  Model  detection  Model  cysts  Model  arterial  Model  Model  lung




              2.2       Year           2017                         2015        2016
              Table   Body  part                                    Chest
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