ArticleJournal of healthcare engineering2018
Breast Mass Detection in Digital Mammogram Based on Gestalt Psychology.
Article in Journal of healthcare engineering, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 41 citations in OpenAlex.
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance.Scientific reports · 2023Article
- Multiple-level thresholding for breast mass detection.Journal of King Saud University. Computer and information sciences · 2023Article
- A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.Scientific reports · 2022Article
- Automatic breast mass detection in mammograms using density of wavelet coefficients and a patch-based CNN.International journal of computer assisted radiology and surgery · 2021Article
- Deep Convolutional Neural Networks-Based Automatic Breast Segmentation and Mass Detection in DCE-MRI.Computational and mathematical methods in medicine · 2020Article
- Context-Aware Superpixel and Bilateral Entropy-Image Coherence Induces Less Entropy.Entropy (Basel, Switzerland) · 2019Article
- Automated Breast Cancer Diagnosis Based on Machine Learning Algorithms.Journal of healthcare engineering · 2019Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Inspired by gestalt psychology, we combine human cognitive characteristics with knowledge of radiologists in medical image analysis. In this paper, a novel framework is proposed to detect breast masses in digitized mammograms. It can be divided into three modules: sensation integration, semantic integration, and verification. After analyzing the progress of radiologist's mammography screening, a series of visual rules based on the morphological characteristics of breast masses are presented and quantified by mathematical methods. The framework can be seen as an effective trade-off between bottom-up sensation and top-down recognition methods. This is a new exploratory method for the automatic detection of lesions. The experiments are performed on Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) data sets. The sensitivity reached to 92% at 1.94 false positive per image (FPI) on MIAS and 93.84% at 2.21 FPI on DDSM. Our framework has achieved a better performance compared with other algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.