Context-Constrained Multiple Instance Learning for Histopathology Image Segmentation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)


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.


Image Segmentation Local Binary Pattern Mucinous Adenocarcinoma Cancer Image Multiple Instance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.State Key Laboratory of Software Development Environment, Key Laboratory of Biomechanics and Mechanobiology of Ministry of EducationBeihang UniversityChina
  2. 2.Microsoft Research AsiaChina
  3. 3.Lab of Neuro Imaging, Department of Neurology and Department of Computer ScienceUCLAUSA
  4. 4.Department of Pathology, School of MedicineZhejiang UniversityChina

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