Realizing Nipple in Profile Recognition and Nipple Detection Using a Single Classification

Nipple in Profile (NIP) is one of the positioned criteria for mammography quality evaluation. The current evaluation work relies on manual reviewing, which is subjective and time-consuming. This paper introduces a novel NIP recognition based on a convolutional neural network (CNN) without referring...

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Bibliographic Details
Main Author: Zeng, Yi-Chong
Format: Conference Proceeding
Language:English
Subjects:
Asia
Convolutional neural networks
Information processing
Manuals
Real-time systems
Task analysis
Online Access:Request full text
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Summary:Nipple in Profile (NIP) is one of the positioned criteria for mammography quality evaluation. The current evaluation work relies on manual reviewing, which is subjective and time-consuming. This paper introduces a novel NIP recognition based on a convolutional neural network (CNN) without referring to prior information, which outputs an objective recognition result in real-time. Amazingly, the added value associated with the proposed method is nipple detection which analyzes the extracted gradient-weighted class activation mapping (Grad-CAM) based on three-category classification. In this work, the defined categories are Nipple in Profile, Nipple not in Profile, and non-nipple. For the NIP recognition, we only exploit the comparison between the Grad-CAMs of Nipple in Profile and Nipple not in Profile. Our method is more creative than the typical classification approaches to realize multiple tasks with a single classification. The experiment results demonstrate that the three-category classification with Densenet-121 achieves better performance in both NIP recognition and nipple detection.
ISSN:2640-0103
DOI:10.1109/APSIPAASC58517.2023.10317394