Page 57 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications
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Journal   Journal       Journal   Journal     Conference  Journal           Continued





                    [103]     [104]         [105]     [106]       [107]       [108]


                    at  Francisco  July  sets  of  total  research  X-ray  for  and  (LIDC-  used,  Dataset  (FMD),  KTH-TIPS-
                    obtained  San  and  data  Institute  10,848  of  set  data  the  adult  35,038  radiographs  chest  Diagnostic  Medical  used  was  Consortium  Initiative  study  the  were  Textures  Database  Group's


                    patients  California,  2013  CXR  Korean  total  of  consisting  Shenzhen  images  by approved  of  chest  frontal  637  the  Sheba  of  Israel,  Resource  for  used  sets  data  of  library  Textures  (KTB),  Research


                    909  January  deidentified  namely,  consisting  set  662  consisting  of  from  Hashomer,  Database  was  Describable  Material  Database  (UIUC)
                    from  of  between  used  data  MC  and  total  of  was  consisting  collected  Department  Image  Database  set  benchmark  Amsterdam  Flickr  Texture  Ponce  the  database
                    images  University  (UCSF)  were  different  used,  Tuberculosis  images,  images,  consisting  used  set  board  posterioreanterior  set  images,  Imaging  Tel  Center,  study  Lung  data  texture  namely,  the  (ALOT),  the  Kylberg  and  Texture

                    1885  the  2015  Three  were  138  Data  ethics  Data  the  The  Image  IDRI)  Six  (DTD),  2b,


                       radiography  X-rays     radiography  X-rays


                    Chest     Chest  (CXR)  Chest     Chest       CT          CT



                    CNN                     CNN       CNN         CNNdmultiple  instance  learning
                    Deep      CNN           Deep      Decaf                   CNN


                       high                             X-             for    diagnosis
                    of  with    of             of     feasibility  chest  a  that  features  malignancy
                    classification  images  a construct  segmentation  semantic  computer-aided  detection  chest  frontal  the  test  in  formulate  framework  deep  cancer  early  the  diseases



                    for  relevant  to  for  supervised  for  and  on  to  pathology  to  transferable  lung  for  lung
                    designed  designed  framework  developed  classification  abnormalities  radiographs  developed  detecting  designed  domain-adaptation  patient-level  prediction  developed  interstitial


                    Model  clinically  fidelity  Model  weakly  Model  System  of  rays  System  learns  Model  of
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