ArticlePatterns (New York, N.Y.)2020
Detecting Asymmetric Patterns and Localizing Cancers on Mammograms.
Article in Patterns (New York, N.Y.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- LBNet: an optimized lightweight CNN for mammographic breast cancer classification with XAI-based interpretability.Scientific reports · 2025Article
- The impact of AI feedback on the accuracy of diagnosis, decision switching and trust in radiography.PloS one · 2025Article
- Reporting radiographers' interaction with Artificial Intelligence-How do different forms of AI feedback impact trust and decision switching?PLOS digital health · 2024Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Deep learning for detection of iso-dense, obscure masses in mammographically dense breasts.European radiology · 2023Article
- A boundary-guided transformer for measuring distance from rectal tumor to anal verge on magnetic resonance images.Patterns (New York, N.Y.) · 2023Article
- Multilevel Modeling of Joint Damage in Rheumatoid Arthritis.Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) · 2022Article
- Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach.PloS one · 2022Article
- Architectural Distortion-Based Digital Mammograms Classification Using Depth Wise Convolutional Neural Network.Biology · 2021Article
- Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
One in eight women develops invasive breast cancer in her lifetime. The frontline protection against this disease is mammography. While computer-assisted diagnosis algorithms have made great progress in generating reliable global predictions, few focus on simultaneously producing regions of interest (ROIs) for biopsy. Can we combine ROI-oriented algorithms with global classification of cancer status, which simultaneously highlight suspicious regions and optimize classification performance? Can the asymmetry of breasts be adopted in deep learning for finding lesions and classifying cancers? We answer the above questions by building deep-learning networks that identify masses and microcalcifications in paired mammograms, exclude false positives, and stepwisely improve performance of the model with asymmetric information regarding the breasts. This method achieved a co-leading place in the Digital Mammography DREAM Challenge for predicting breast cancer. We highlight here the importance of this dual-purpose process that simultaneously provides the locations of potential lesions in mammograms.
Identifiers
What OpenQuestion holds
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.