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Cluster based statistical feature extraction method for automatic bleeding detection in wireless capsule endoscopy video.

Author(s): Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO

Comput Biol Med. 2018 03 01;94:41-54 Authors: Ghosh T, Fattah SA, Wahid KA, Zhu WP, Ahmad MO

Article GUID: 29407997
Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Author(s): Yousefi M, Krzyzak A, Suen CY

Comput Biol Med. 2018 05 01;96:283-293 Authors: Yousefi M, Krzyżak A, Suen CY

Article GUID: 29665537
Title:Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.
Authors:Yousefi M,  Krzyzak A,  Suen CY
Link:https://www.ncbi.nlm.nih.gov/pubmed/29665537?dopt=Abstract
Category:Comput Biol Med
PMID:29665537
Dept Affiliation: ENCS
1 Department of Computer Science and Software Engineering Concordia University, 1455 De Maisonneuve Blvd. W, Montreal, Quebec H3G 1M8, Canada. Electronic address: mi_yous@encs.concordia.ca.
2 Department of Computer Science and Software Engineering Concordia University, 1455 De Maisonneuve Blvd. W, Montreal, Quebec H3G 1M8, Canada.

Description:

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Comput Biol Med. 2018 05 01;96:283-293

Authors: Yousefi M, Krzyzak A, Suen CY

Abstract

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs.

PMID: 29665537 [PubMed - indexed for MEDLINE]