ArticleACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine2019
Fusion in Breast Cancer Histology Classification.
Article in ACM-BCB ... ... : the ... ACM Conference on Bioinformatics, Computational Biology and Biomedicine. ACM Conference on Bioinformatics, Computational Biology and Biomedicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Automated Segmentation and Analysis of Histopathological Breast Cancer Images for Enhanced IDC Diagnosis and Assessment Using MobileNetV2+U-Net With Label Propagation.International journal of breast cancer · 2026Article
- Embedding-driven dual-branch approach for accurate breast tumor cellularity classification.Scientific reports · 2025Article
- Breast cancer detection based on histological images using fusion of diffusion model outputs.Scientific reports · 2025Article
- Transformative Approaches in Breast Cancer Detection: Integrating Transformers into Computer-Aided Diagnosis for Histopathological Classification.Bioengineering (Basel, Switzerland) · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- Deep Learning Application for Analyzing of Constituents and Their Correlations in the Interpretations of Medical Images.Diagnostics (Basel, Switzerland) · 2021Review
- Generating Region of Interests for Invasive Breast Cancer in Histopathological Whole-Slide-Image.Proceedings : Annual International Computer Software and Applications Conference. COMPSAC · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
Breast cancer is a deadly disease that affects millions of women worldwide. The International Conference on Image Analysis and Recognition in 2018 presents the BreAst Cancer Histology (ICIAR2018 BACH) image data challenge that calls for computer tools to assist pathologists and doctors in the clinical diagnosis of breast cancer subtypes. Using the BACH dataset, we have developed an image classification pipeline that combines both a shallow learner (support vector machine) and a deep learner (convolutional neural network). The shallow learner and deep learners achieved moderate accuracies of 79% and 81% individually. When being integrated by fusion algorithms, the system outperformed any individual learner with the highest accuracy as 92%. The fusion presents big potential for improving clinical design support.
Indexed as
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.