Automated Grading of Breast Cancer Histopathology Images Using Multilayered Autoencoder
Source abstract
Abstract
Breast cancer (BC) is the most widely recognized cancer in women worldwide. By 2018, 627,000 women had died of breast cancer (World Health Organization Report 2018). To diagnose BC, the evaluation of tumours is achieved by analysis of histological specimens. At present, the Nottingham Bloom Richardson framework is the least expensive approach used to grade BC aggressiveness. Pathologists contemplate three elements, 1. mitotic count, 2. gland formation, and 3. nuclear atypia, which is a laborious process that witness's variations in expert's opinions. Recently, some algorithms have been proposed for the detection of mitotic cells, but nuclear atypia in breast cancer histopathology has not received much consideration. Nuclear atypia analysis is performed not only to grade BC but also to provide critical information in the discrimination of normal breast, non-invasive breast (usual ductal hyperplasia, atypical ductal hyperplasia) and pre-invasive breast (ductal carcinoma in situ) and invasive breast lesions. We proposed a deep-stacked multi-layer autoencoder ensemble with a softmax layer for the feature extraction and classification process. The classification results show the value of the multi-layer autoencoder model in the evaluation of nuclear polymorphisms. The proposed method has indicated promising results, making them more fit in breast cancer grading.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: AI in cancer detection · Digital Imaging for Blood Diseases · Radiomics and Machine Learning in Medical Imaging
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (57)
Show source-provided references
1. A global covariance descriptor for nuclear atypia scoring in breast histopathology images10.1109/JBHI.2015.2447008 ↗
2. Cancer statistics, 2016
3. Efficient segmentation of a breast in B-mode ultrasound tomography using three-dimensional grabCut (GC3D)10.3390/s17081827 ↗
4. Identification of breast malignancy by marker-controlled watershed transformation and hybrid feature set for healthcare10.3390/app10061900 ↗
5. Automatic cell nuclei segmentation and classification of breast cancer histopathology images10.1016/j.sigpro.2015.11.011 ↗
6. Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: Experience from a large study with long-term follow-up10.1111/j.1365-2559.1991.tb00229.x ↗
7. Methods for nuclei detection, segmentation, and classification in digital histopathology: A review-current status and future potential10.1109/RBME.2013.2295804 ↗
8. Texture measures combination for improved meningioma classification of histopathological images10.1016/j.patcog.2010.01.005 ↗
9. Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma
10. Applied sciences development of a mortality risk model in elderly hip fracture patients by different analytical approaches10.3390/app10196787 ↗
11. Treatment outcomes of patients with adenocarcinoma of the uterine cervix after definitive radiotherapy and the prognostic impact of tumor-infiltrating CD8+ lymphocytes in pre-treatment biopsy specimens: A multi-institutional retrospective study10.1093/jrr/rrz106 ↗
12. Automatic detection of pulmonary nodules using three-dimensional chain coding and optimized random forest10.3390/app10072346 ↗
13. Classification and staging of chronic liver disease from multimodal data10.1109/TBME.2012.2235438 ↗
14. A novel bio-inspired method for early diagnosis of breast cancer through mammographic image analysis10.3390/app9214492 ↗
15. Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features10.1016/j.neucom.2016.05.084 ↗
16. Double-shot transfer learning for breast cancer classification from x-ray images10.3390/app10113999 ↗
17. Automated mitosis detection in histopathology using morphological and multi-channel statistics features10.4103/2153-3539.112695 ↗
18. Efficient mitosis detection in breast cancer histology images by RCNN
19. Digital mammographic tumor classification using transfer learning from deep convolutional neural networks10.1117/1.JMI.3.3.034501 ↗
20. Multiple instance learning for histopathological breast cancer image classification10.1016/j.eswa.2018.09.049 ↗
21. A novel system for scoring of hormone receptors in breast cancer histopathology slides
22. Circulating tumor cells in metastatic breast cancer: Clinical applications and future possibilities10.3390/app10093311 ↗
23. Whole-slide mitosis detection in H&E breast histology using PHH3 as a reference to train distilled stain-invariant convolutional networks10.1109/TMI.2018.2820199 ↗
24. A dataset for breast cancer histopathological image classification10.1109/TBME.2015.2496264 ↗
25. Weakly-supervised classification of HER2 expression in breast cancer haematoxylin and eosin stained slides10.3390/app10144728 ↗
26. Mitosis detection in breast cancer histology images using deep neural networks
27. Automated mitosis detection using texture, SIFT features and HMAX biologically inspired approach10.4103/2153-3539.109870 ↗
28. Crowdsourcing image annotation for nucleus detection and segmentation in computational pathology
29. Geodesic geometric mean of regional covariance descriptors as an image-level descriptor for nuclear atypia grading in breast histology images
30. Automated grading of breast cancer histopathology using spectral clustering with textural and architectural image features
31. Performance analysis and detection of micro calcification in digital mammograms using wavelet features
32. Artificial intelligence-based mitosis detection in breast cancer histopathology images using faster R-cNN and deep CNNs10.3390/jcm9030749 ↗
33. Deep learning for semantic segmentation vs. classification in computational pathology: Application to mitosis analysis in breast cancer grading10.3389/fbioe.2019.00145 ↗
34. Disentangled autoencoder for cross-stain feature extraction in pathology image analysis10.3390/app10186427 ↗
35. L. Roux, D. Racoceanu, F. Capron, J. Calvo, E. Attieh et al., “MITOS & ATYPIA detection of mitosis and evaluation of nuclear atypia score in breast cancer histological images, 2014. [Online]. Available: https://mitos-atypia-14.grand-challenge.org”.
36. Virtual adversarial training: A regularization method for supervised and semi-supervised learning10.1109/TPAMI.2018.2858821 ↗
37. The effectiveness of data augmentation in image classification using deep learning
38. Unsupervised data augmentation for consistency training
39. Research on data augmentation for image classification based on convolution neural networks
40. Data augmentation methods for machine-learning-based classification of bio-signals
41. Patch-based convolutional neural network for whole slide tissue image classification
42. Histologic grading of breast carcinoma: A multi-institution study of interobserver variation using virtual microscopy10.1038/s41379-020-00698-2 ↗
43. Density-aware local siamese autoencoder network embedding with autoencoder graph clustering
44. Kernel based learning approach for satellite image classification using support vector machine
45. Stacked sparse autoencoder (SSAE) based framework for nuclei patch classification on breast cancer histopathology
46. An ensemble machine learning technique for functional requirement classification10.3390/sym12101601 ↗
47. An automated and intelligent medical decision support system for brain MRI scans classification10.1371/journal.pone.0135875 ↗
48. Automatic generation of bioinformatics tools for predicting protein-ligand binding sites10.1093/bioinformatics/btv593 ↗
49. Comparative study of machine learning algorithms for breast cancer detection and diagnosis
50. Self-universum support vector machine10.1007/s00779-014-0797-9 ↗
51. Data mining for classification of power quality problems using WEKA and the effect of attributes on classification accuracy
52. Predicting the pillar stability of underground mines with random trees and C4.5 decision trees10.3390/app10186486 ↗
53. Application of decision tree for MRI images of premature brain injury classification
54. Multi-class disease classification in brain MRIs using a computer-aided diagnostic system10.3390/sym9030037 ↗
55. Reversible data hiding using multi-layer perceptron based pixel prediction
56. Breast cancer histopathology image analysis: A review10.1109/TBME.2014.2303852 ↗
57. Stacked sparse autoencoder (SSAE) for nuclei detection on breast cancer histopathology images10.1109/TMI.2015.2458702 ↗