Convolutional neural networks for false positive reduction of automatically detected cilia in low magnification TEM images

• Automated detection of cilia in low magnification transmission electron microscopy images is a central task in the quest to relieve the pathologists in the manual, time consuming and subjective diagnostic procedure. However, automation of the process, specifically in low magnification, is challenging due to the similar characteristics of non-cilia candidates. In this paper, a convolutional neural network classifier is proposed to further reduce the false positives detected by a previously presented template matching method. Adding the proposed convolutional neural network increases the area under Precision-Recall curve from 0.42 to 0.71, and significantly reduces the number of false positive objects.

Subject headings

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Klinisk laboratoriemedicin (hsv//swe)

MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Clinical Laboratory Medicine (hsv//eng)

Keyword

Computerized Image Processing

Datoriserad bildbehandling

Patologi

Pathology

Publication and Content Type

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