title: Computer-aided diagnosis from weak supervision: a benchmarking study.
abstract: Supervised machine learning is a powerful tool frequently used in computer-aided diagnosis (CAD) applications. The bottleneck of this technique is its demand for fine grained expert annotations, which are tedious for medical image analysis applications. Furthermore, information is typically localized in diagnostic images, which makes representation of an entire image by a single feature set problematic. The multiple instance learning framework serves as a remedy to these two problems by allowing labels to be provided for groups of observations, called bags, and assuming the group label to be the maximum of the instance labels within the bag. This setup can effectively be applied to CAD by splitting a given diagnostic image into a Cartesian grid, treating each grid element (patch) as an instance by representing it with a feature set, and grouping instances belonging to the same image into a bag. We quantify the power of existing multiple instance learning methods by evaluating their performance on two distinct CAD applications: (i) Barrett's cancer diagnosis and (ii) diabetic retinopathy screening. In the experiments, mi-Graph appears as the best-performing method in bag-level prediction (i.e. diagnosis) for both of these applications that have drastically different visual characteristics. For instance-level prediction (i.e. disease localization), mi-SVM ranks as the most accurate method.
authors.affiliation: Heidelberg University, HCI, Speyerer Str. 6, D-69115 Heidelberg, Germany. Electronic address: melih.kandemir@iwr.uni-heidelberg.de.
authors.forename: Melih
authors.initials: M
authors.lastname: Kandemir
authors.affiliation: Heidelberg University, HCI, Speyerer Str. 6, D-69115 Heidelberg, Germany. Electronic address: fred.hamprecht@uni-heidelberg.de.
authors.forename: Fred A
authors.initials: FA
authors.lastname: Hamprecht
mesh_terms: D000465:Algorithms; D019985:Benchmarking; D003930:Diabetic Retinopathy / Q000473:pathology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008853:Microscopy / Q000379:methods* / Q000592:standards; D009369:Neoplasms / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D000069553:Supervised Machine Learning
keywords: Cancer diagnosis; Diabetic retinopathy screening; Multiple instance learning