ArticleComputational and mathematical methods in medicine2021
A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization.
Article in Computational and mathematical methods in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 21 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
21 citing papers in PubMed.
- Computational pathology in breast cancer: optimizing molecular prediction through task-oriented AI models.NPJ breast cancer · 2025Review
- RETRACTED: Skin cancer segmentation and classification by implementing a hybrid FrCN-(U-NeT) technique with machine learning.PloS one · 2025Article
- Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.Journal of pathology informatics · 2024Article
- An improved breast cancer classification with hybrid chaotic sand cat and Remora Optimization feature selection algorithm.PloS one · 2024Article
- Retracted: A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization.Computational and mathematical methods in medicine · 2023Article
- Developing an Efficient Cancer Detection and Prediction Tool Using Convolution Neural Network Integrated with Neural Pattern Recognition.BioMed research international · 2023Article
- Classification and Detection of Mesothelioma Cancer Using Feature Selection-Enabled Machine Learning Technique.BioMed research international · 2022Article
- Machine Learning-Based Multimodel Computing for Medical Imaging for Classification and Detection of Alzheimer Disease.Computational intelligence and neuroscience · 2022Article
- Deep Neural Network-Based Novel Mathematical Model for 3D Brain Tumor Segmentation.Computational intelligence and neuroscience · 2022Article
- Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.BioMed research international · 2022Article
- Deep Learning-Based Real-Time Discriminate Correlation Analysis for Breast Cancer Detection.BioMed research international · 2022Article
- Multidimensional CNN-Based Deep Segmentation Method for Tumor Identification.BioMed research international · 2022Article
- Lung Cancer Classification and Prediction Using Machine Learning and Image Processing.BioMed research international · 2022Article
- Blockchain Framework for Secure COVID-19 Pandemic Data Handling and Protection.Computational intelligence and neuroscience · 2022Article
- A Lightweight Hybrid Dilated Ghost Model-Based Approach for the Prognosis of Breast Cancer.Computational intelligence and neuroscience · 2022Article
- PSO-Based Evolutionary Approach to Optimize Head and Neck Biomedical Image to Detect Mesothelioma Cancer.BioMed research international · 2022Article
- Attention Layer-Based Multidimensional Feature Extraction for Diagnosis of Lung Cancer.BioMed research international · 2022Article
- Appropriate Supervised Machine Learning Techniques for Mesothelioma Detection and Cure.BioMed research international · 2022Article
- Identification and Classification of Prostate Cancer Identification and Classification Based on Improved Convolution Neural Network.BioMed research international · 2022Article
- Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.BioMed research international · 2022Article
Corrections and comments
- Retraction · 2023-12-13Compromised Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for "transfer learning" in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.
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.