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Proceedings Paper

Detecting multiple myeloma via generalized multiple-instance learning
Author(s): Jan Hering; Jan Kybic; Lukáš Lambert
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Paper Abstract

We address the task of automatic detection of lesions caused by multiple myeloma (MM) in femurs or other long bones from CT data. Such detection is already an important part of the multiple myeloma diagnosis and staging. However, it is so far performed mostly manually, which is very time consuming. We formulate the detection as a multiple instance learning (MIL) problem, where instances are grouped into bags and only bag labels are available. In our case, instances are regions in the image and bags correspond to images. This has the advantage of requiring only subject-level annotation (ground truth), which is much easier to get than voxel-level manual segmentation. We consider a generalization of the standard MIL formulation where we introduce a threshold on the number of required positive instances in positive bags. This corresponds better to the classification procedure used by the radiology experts and is more robust with respect to false positive instances. We extend several existing MIL algorithms to solve the generalized case by estimating the threshold during learning. We compare the proposed methods with the baseline method on a dataset of 220 subjects. We show that the generalized MIL formulation outperforms standard MIL methods for this task. For the task of distinguishing between healthy controls and MM patients with infiltrations, our best method makes almost no mistakes with a mean AUC of 0.982 and F1 = 0.965. We outperform the baseline method significantly in all conducted experiments.

Paper Details

Date Published: 2 March 2018
PDF: 6 pages
Proc. SPIE 10574, Medical Imaging 2018: Image Processing, 105740N (2 March 2018); doi: 10.1117/12.2293112
Show Author Affiliations
Jan Hering, Czech Technical Univ. in Prague (Czech Republic)
Jan Kybic, Czech Technical Univ. in Prague (Czech Republic)
Lukáš Lambert, Charles Univ. in Prague (Czech Republic)

Published in SPIE Proceedings Vol. 10574:
Medical Imaging 2018: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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