ArticleFrontiers in genetics2020
Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs).
Article in Frontiers in genetics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- BC-SwinNet: Swin transformer and CNN with multi-objective optimization for multi-class breast cancer detection using Histopathological images.Scientific reports · 2026Article
- A mutual reconstruction network model for few-shot classification of histological images: addressing interclass similarity and intraclass diversity.Quantitative imaging in medicine and surgery · 2024Article
- Bibliometric analysis of the application of deep learning in cancer from 2015 to 2023.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Fine-grained image classification on bats using VGG16-CBAM: a practical example with 7 horseshoe bats taxa (CHIROPTERA: Rhinolophidae: Rhinolophus) from Southern China.Frontiers in zoology · 2024Article
- Measuring the binary thickness of buccal bone of anterior maxilla in low-resolution cone-beam computed tomography via a bilinear convolutional neural network.Quantitative imaging in medicine and surgery · 2023Article
- Deep-Stacked Convolutional Neural Networks for Brain Abnormality Classification Based on MRI Images.Journal of digital imaging · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.Diagnostics (Basel, Switzerland) · 2022Article
- Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.PloS one · 2022Article
- Automatic breast carcinoma detection in histopathological micrographs based on Single Shot Multibox Detector.Journal of pathology informatics · 2022Article
- Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention.BioMed research international · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Classification of histopathological images of cancer is challenging even for well-trained professionals, due to the fine-grained variability of the disease. Deep Convolutional Neural Networks (CNNs) showed great potential for classification of a number of the highly variable fine-grained objects. In this study, we introduce a Bilinear Convolutional Neural Networks (BCNNs) based deep learning method for fine-grained classification of breast cancer histopathological images. We evaluated our model by comparison with several deep learning algorithms for fine-grained classification. We used bilinear pooling to aggregate a large number of orderless features without taking into consideration the disease location. The experimental results on BreaKHis, a publicly available breast cancer dataset, showed that our method is highly accurate with 99.24% and 95.95% accuracy in binary and in fine-grained classification, respectively.
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.