title: Context-constrained multiple instance learning for histopathology image segmentation.
abstract: Histopathology image segmentation plays a very important role in cancer diagnosis and therapeutic treatment. Existing supervised approaches for image segmentation require a large amount of high quality manual delineations (on pixels), which is often hard to obtain. In this paper, we propose a new algorithm along the line of weakly supervised learning; we introduce context constraints as a prior for multiple instance learning (ccMIL), which significantly reduces the ambiguity in weak supervision (a 20% gain); our method utilizes image-level labels to learn an integrated model to perform histopathology cancer image segmentation, clustering, and classification. Experimental results on colon histopathology images demonstrate the great advantages of ccMIL.
authors.affiliation: State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of Education, Beihang University, China.
authors.forename: Yan
authors.initials: Y
authors.lastname: Xu
authors.forename: Jianwen
authors.initials: J
authors.lastname: Zhang
authors.forename: Eric I-Chao
authors.initials: EI
authors.lastname: Chang
authors.forename: Maode
authors.initials: M
authors.lastname: Lai
authors.forename: Zhuowen
authors.initials: Z
authors.lastname: Tu
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003110:Colonic 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