A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology
Classification
- URL: http://arxiv.org/abs/2308.02781v2
- Date: Tue, 8 Aug 2023 14:54:36 GMT
- Title: A Voting-Stacking Ensemble of Inception Networks for Cervical Cytology
Classification
- Authors: Linyi Qian, Qian Huang, Yulin Chen, Junzhou Chen
- Abstract summary: Cervical cancer is one of the most severe diseases threatening women's health.
We propose a Voting-Stacking ensemble strategy, which employs three Inception networks as base learners and integrates their outputs through a voting ensemble.
The experimental results outperform the current state-of-the-art (SOTA) methods, demonstrating its potential for reducing screening workload and helping pathologists detect cervical cancer.
- Score: 10.61705267657852
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Cervical cancer is one of the most severe diseases threatening women's
health. Early detection and diagnosis can significantly reduce cancer risk, in
which cervical cytology classification is indispensable. Researchers have
recently designed many networks for automated cervical cancer diagnosis, but
the limited accuracy and bulky size of these individual models cannot meet
practical application needs. To address this issue, we propose a
Voting-Stacking ensemble strategy, which employs three Inception networks as
base learners and integrates their outputs through a voting ensemble. The
samples misclassified by the ensemble model generate a new training set on
which a linear classification model is trained as the meta-learner and performs
the final predictions. In addition, a multi-level Stacking ensemble framework
is designed to improve performance further. The method is evaluated on the
SIPakMed, Herlev, and Mendeley datasets, achieving accuracies of 100%, 100%,
and 100%, respectively. The experimental results outperform the current
state-of-the-art (SOTA) methods, demonstrating its potential for reducing
screening workload and helping pathologists detect cervical cancer.
Related papers
- Cervical Cancer Detection Using Multi-Branch Deep Learning Model [0.6249768559720121]
This research proposes an innovative and novel approach to automate cervical cancer image classification using Multi-Head Self-Attention (MHSA) and convolutional neural networks (CNNs)
Our model achieved a remarkable accuracy of 98.522%, which holds promise for its applicability in other medical image recognition tasks.
arXiv Detail & Related papers (2024-08-20T02:44:48Z) - Multi-task Explainable Skin Lesion Classification [54.76511683427566]
We propose a few-shot-based approach for skin lesions that generalizes well with few labelled data.
The proposed approach comprises a fusion of a segmentation network that acts as an attention module and classification network.
arXiv Detail & Related papers (2023-10-11T05:49:47Z) - Learning to diagnose cirrhosis from radiological and histological labels
with joint self and weakly-supervised pretraining strategies [62.840338941861134]
We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset.
We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis.
This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
arXiv Detail & Related papers (2023-02-16T17:06:23Z) - A Pathologist-Informed Workflow for Classification of Prostate Glands in
Histopathology [62.997667081978825]
Pathologists diagnose and grade prostate cancer by examining tissue from needle biopsies on glass slides.
Cancer's severity and risk of metastasis are determined by the Gleason grade, a score based on the organization and morphology of prostate cancer glands.
This paper proposes an automated workflow that follows pathologists' textitmodus operandi, isolating and classifying multi-scale patches of individual glands.
arXiv Detail & Related papers (2022-09-27T14:08:19Z) - Application of Transfer Learning and Ensemble Learning in Image-level
Classification for Breast Histopathology [9.037868656840736]
In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features.
This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions.
Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90%$.
arXiv Detail & Related papers (2022-04-18T13:31:53Z) - Multi-Scale Hybrid Vision Transformer for Learning Gastric Histology:
AI-Based Decision Support System for Gastric Cancer Treatment [50.89811515036067]
Gastric endoscopic screening is an effective way to decide appropriate gastric cancer (GC) treatment at an early stage, reducing GC-associated mortality rate.
We propose a practical AI system that enables five subclassifications of GC pathology, which can be directly matched to general GC treatment guidance.
arXiv Detail & Related papers (2022-02-17T08:33:52Z) - Open-Set Recognition of Breast Cancer Treatments [91.3247063132127]
Open-set recognition generalizes a classification task by classifying test samples as one of the known classes from training or "unknown"
We apply a recent existing Gaussian mixture variational autoencoder model, which achieves state-of-the-art results for image datasets, to breast cancer patient data.
Not only do we obtain more accurate and robust classification results, with a 24.5% average F1 increase compared to a recent method, but we also reexamine open-set recognition in terms of deployability to a clinical setting.
arXiv Detail & Related papers (2022-01-09T04:35:55Z) - Ensemble of CNN classifiers using Sugeno Fuzzy Integral Technique for
Cervical Cytology Image Classification [1.6986898305640261]
We propose a fully automated computer-aided diagnosis tool for classifying single-cell and slide images of cervical cancer.
We use the Sugeno Fuzzy Integral to ensemble the decision scores from three popular deep learning models, namely, Inception v3, DenseNet-161 and ResNet-34.
arXiv Detail & Related papers (2021-08-21T08:41:41Z) - Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype
Prediction [55.94378672172967]
We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients.
We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks.
Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
arXiv Detail & Related papers (2020-09-02T02:50:30Z) - Semi-Supervised Cervical Dysplasia Classification With Learnable Graph
Convolutional Network [25.685255609487623]
Digital cervicography has great potential as a primary or auxiliary screening tool.
Traditional fully-supervised training of such systems requires large amounts of annotated data.
We propose a novel graph convolutional network (GCN) based semi-supervised classification model.
arXiv Detail & Related papers (2020-04-01T01:53:26Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.