Interpretable pap smear cell representation for cervical cancer screening

• URL: http://arxiv.org/abs/2311.10269v1
• Date: Fri, 17 Nov 2023 01:29:16 GMT
• Title: Interpretable pap smear cell representation for cervical cancer screening
• Authors: Yu Ando and Nora Jee-Young Park and, Gun Oh Chong and Seokhwan Ko and Donghyeon Lee and Junghwan Cho and Hyungsoo Han
• Abstract summary: We introduce a method to learn explainable deep cervical cell representations for pap smear images based on one class classification using variational autoencoders. Our model can discriminate abnormality without the need of additional training of deep models.
• Score: 3.8656297418166305
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Screening is critical for prevention and early detection of cervical cancer but it is time-consuming and laborious. Supervised deep convolutional neural networks have been developed to automate pap smear screening and the results are promising. However, the interest in using only normal samples to train deep neural networks has increased owing to class imbalance problems and high-labeling costs that are both prevalent in healthcare. In this study, we introduce a method to learn explainable deep cervical cell representations for pap smear cytology images based on one class classification using variational autoencoders. Findings demonstrate that a score can be calculated for cell abnormality without training models with abnormal samples and localize abnormality to interpret our results with a novel metric based on absolute difference in cross entropy in agglomerative clustering. The best model that discriminates squamous cell carcinoma (SCC) from normals gives 0.908 +- 0.003 area under operating characteristic curve (AUC) and one that discriminates high-grade epithelial lesion (HSIL) 0.920 +- 0.002 AUC. Compared to other clustering methods, our method enhances the V-measure and yields higher homogeneity scores, which more effectively isolate different abnormality regions, aiding in the interpretation of our results. Evaluation using in-house and additional open dataset show that our model can discriminate abnormality without the need of additional training of deep models.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• Holistic and Historical Instance Comparison for Cervical Cell Detection [6.735336269995631]
  We propose a holistic and historical instance comparison approach for cervical cell detection. Specifically, we first develop a holistic instance comparison scheme enforcing both RoI-level and class-level cell discrimination. This coarse-to-fine cell comparison encourages the model to learn foreground-distinguishable and class-wise representations.
  arXiv  Detail & Related papers  (2024-09-21T02:36:19Z)
• Deep Learning Descriptor Hybridization with Feature Reduction for Accurate Cervical Cancer Colposcopy Image Classification [0.9374652839580183]
  We propose a novel Computer Aided Diagnosis (CAD) system that combines the strengths of various deep-learning descriptors with appropriate feature normalization. Our approach achieves exceptional performance in the range of 97%-100% for both the normal-abnormal and the type classification.
  arXiv  Detail & Related papers  (2024-05-01T06:05:13Z)
• Corneal endothelium assessment in specular microscopy images with Fuchs' dystrophy via deep regression of signed distance maps [48.498376125522114]
  This paper proposes a UNet-based segmentation approach that requires minimal post-processing. It achieves reliable CE morphometric assessment and guttae identification across all degrees of Fuchs' dystrophy.
  arXiv  Detail & Related papers  (2022-10-13T15:34:20Z)
• Oral cancer detection and interpretation: Deep multiple instance learning versus conventional deep single instance learning [2.2612425542955292]
  Current medical standard for setting an oral cancer (OC) diagnosis is histological examination of a tissue sample from the oral cavity. To introduce this approach into clinical routine is associated with challenges such as a lack of experts and labour-intensive work. We are interested in AI-based methods that reliably can detect cancer given only per-patient labels.
  arXiv  Detail & Related papers  (2022-02-03T15:04:26Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Wide & Deep neural network model for patch aggregation in CNN-based prostate cancer detection systems [51.19354417900591]
  Prostate cancer (PCa) is one of the leading causes of death among men, with almost 1.41 million new cases and around 375,000 deaths in 2020. To perform an automatic diagnosis, prostate tissue samples are first digitized into gigapixel-resolution whole-slide images. Small subimages called patches are extracted and predicted, obtaining a patch-level classification.
  arXiv  Detail & Related papers  (2021-05-20T18:13:58Z)
• Acute Lymphoblastic Leukemia Detection from Microscopic Images Using Weighted Ensemble of Convolutional Neural Networks [4.095759108304108]
  This article has automated the ALL detection task from microscopic cell images, employing deep Convolutional Neural Networks (CNNs) Various data augmentations and pre-processing are incorporated for achieving a better generalization of the network. Our proposed weighted ensemble model, using the kappa values of the ensemble candidates as their weights, has outputted a weighted F1-score of 88.6 %, a balanced accuracy of 86.2 %, and an AUC of 0.941 in the preliminary test set.
  arXiv  Detail & Related papers  (2021-05-09T18:58:48Z)
• Bootstrapping Your Own Positive Sample: Contrastive Learning With Electronic Health Record Data [62.29031007761901]
  This paper proposes a novel contrastive regularized clinical classification model. We introduce two unique positive sampling strategies specifically tailored for EHR data. Our framework yields highly competitive experimental results in predicting the mortality risk on real-world COVID-19 EHR data.
  arXiv  Detail & Related papers  (2021-04-07T06:02:04Z)
• DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques [14.208643185430219]
  Cervical cancer, one of the most common fatal cancers among women, can be prevented by regular screening to detect any precancerous lesions at early stages. To improve the manual screening practice, machine learning (ML) and deep learning (DL) based computer-aided diagnostic (CAD) systems have been investigated to classify cervical pap cells. This study proposes a hybrid deep feature fusion (HDFF) technique based on DL to classify the cervical cells accurately.
  arXiv  Detail & Related papers  (2021-02-24T10:34:51Z)
• Improved Slice-wise Tumour Detection in Brain MRIs by Computing Dissimilarities between Latent Representations [68.8204255655161]
  Anomaly detection for Magnetic Resonance Images (MRIs) can be solved with unsupervised methods. We have proposed a slice-wise semi-supervised method for tumour detection based on the computation of a dissimilarity function in the latent space of a Variational AutoEncoder. We show that by training the models on higher resolution images and by improving the quality of the reconstructions, we obtain results which are comparable with different baselines.
  arXiv  Detail & Related papers  (2020-07-24T14:02:09Z)

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.