title: Empowering multiple instance histopathology cancer diagnosis by cell graphs.
abstract: We introduce a probabilistic classifier that combines multiple instance learning and relational learning. While multiple instance learning allows automated cancer diagnosis from only image-level annotations, relational learning allows exploiting changes in cell formations due to cancer. Our method extends Gaussian process multiple instance learning with a relational likelihood that brings improved diagnostic performance on two tissue microarray data sets (breast and Barrett's cancer) when similarity of cell layouts in different tissue regions is used as relational side information.
authors.forename: Melih
authors.initials: M
authors.lastname: Kandemir
authors.forename: Chong
authors.initials: C
authors.lastname: Zhang
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001471:Barrett Esophagus / Q000473:pathology*; D001943:Breast Neoplasms / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique