ArticleBioMed research international2022
Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.
Article in BioMed research international, 2022. 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 It has been retracted, and should not be counted.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 7 papers, 1 of them a synthesis that pooled it.
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, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- Machine learning and deep learning-based approach in smart healthcare: Recent advances, applications, challenges and opportunities.AIMS public health · 2024Article
- Retracted: Cyclic GAN Model to Classify Breast Cancer Data for Pathological Healthcare Task.BioMed research international · 2024Article
- Bridged-U-Net-ASPP-EVO and Deep Learning Optimization for Brain Tumor Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Hybrid Majority Voting: Prediction and Classification Model for Obesity.Diagnostics (Basel, Switzerland) · 2023Article
- U-Net-Based Models towards Optimal MR Brain Image Segmentation.Diagnostics (Basel, Switzerland) · 2023Article
- Effectiveness of a diagnostic algorithm for dengue based on an artificial neural network.Digital healthArticle
Corrections and comments
- Retraction · 2024-01-09Concerns/Issues about Data · Concerns/Issues about Results and/or Conclusions · Concerns/Issues about Referencing/Attributions · Concerns/Issues about Peer Review · Investigation by Journal/Publisher · Investigation by Third Party · Paper Mill · Computer-Aided Content or Computer-Generated Content · Unreliable Results and/or Conclusions ·
- Retracted
Authors and funding
6 authors at 6 institutions in 5 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
An algorithm framework based on CycleGAN and an upgraded dual-path network (DPN) is suggested to address the difficulties of uneven staining in pathological pictures and difficulty of discriminating benign from malignant cells. CycleGAN is used for color normalization in pathological pictures to tackle the problem of uneven staining. However, the resultant detection model is ineffective. By overlapping the images, the DPN uses the addition of small convolution, deconvolution, and attention mechanisms to enhance the model's ability to classify the texture features of pathological images on the BreaKHis dataset. The parameters that are taken into consideration for measuring the accuracy of the proposed model are false-positive rate, false-negative rate, recall, precision, and
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.