Abstract
In the Gastric Histopathology Image Classification (GHIC) tasks, which are usually weakly supervised learning missions, there is inevitably redundant information in the images. Therefore, designing networks that can focus on distinguishing features has become a popular research topic. In this paper, to accomplish the tasks of GHIC superiorly and assist pathologists in clinical diagnosis, an intelligent Hierarchical Conditional Random Field based Attention Mechanism (HCRF-AM) model is proposed. The HCRF-AM model consists of an Attention Mechanism (AM) module and an Image Classification (IC) module. In the AM module, an HCRF model is built to extract attention regions. In the IC module, a Convolutional Neural Network (CNN) model is trained with the attention regions selected, and then an algorithm called Classification Probability-based Ensemble Learning is applied to obtain the image-level results from the patch-level output of the CNN. In the experiment, a classification specificity of 96.67% is achieved on a gastric histopathology dataset with 700 images. Our HCRF-AM model demonstrates high classification performance and shows its effectiveness and future potential in the GHIC field. In addition, the AM module and transfer learning technique allow the network to generalize well to other types of image data except histopathology images, and we obtain 95.5% and 95.8% accuracies on IG02 and Oxford-IIIT Pet Datasets.
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References
Wild C, Stewart B, Wild C (2014) World Cancer Report, World health organization, Geneva, Switzerland
Bray F, Ferlay J, Soerjomataram I, Siegel R, Torre L, Jemal A (2018) Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA:, A Cancer Journal for Clinicians 68(6):394–424
Orditura M, Galizia G, Sforza V, Gambardella V, Fabozzi A, Laterza M, Andreozzi F, Ventriglia J, Savastano B, Mabilia A et al (2014) Treatment of gastric cancer. World Journal of Gastroenterology:, WJG 20(7):1635
Van Cutsem E, Sagaert X, Topal B, Haustermans K, Prenen H (2016) Gastric Cancer. The Lancet 388(10060):2654–2664
Elsheikh T, Austin M, Chhieng D, Miller F, Moriarty A, Renshaw A (2013) American society of cytopathology workload recommendations for automated pap test screening: developed by the productivity and quality assurance in the era of automated screening task force. Diagn Cytopathol 41(2):174–178
Wang H, Jia H, Lu L, **a Y (2019) Thorax-net: an attention regularized deep neural network for classification of thoracic diseases on chest radiography. IEEE Journal of Biomedical and Health Informatics 24(2):475–485
Li L, Xu M, Wang X, Jiang L, Liu H (2019) Attention Based Glaucoma detection: A large-scale Database and CNN Model. In: Proc. of CVPR, 2019, pp 10571–10580
Sun C, Li C, Zhang J, Rahaman M, Ai S, Chen H, Kulwa F, Li Y, Li X, Jiang T (2020) Gastric histopathology image segmentation using a hierarchical conditional random field. Biocybernetics and Biomedical Engineering 40(4):1535–1555
Sun C, Li C, Zhang J, Kulwa F, Li X (2020) Hierarchical Conditional Random Field Model for Multi-object Segmentation in Gastric Histopathology Images. Electron Lett 56(15):750–753
Zhu R, Zhang R, Xue D (2015) Lesion detection of endoscopy images based on convolutional neural network features. In: 2015 8th International Congress on Image and Signal Processing (CISP), pp 372–376
Ishihara K, Ogawa T, Haseyama M (2017) Detection of Gastric Cancer Risk from X-ray Images via Patch-based Convolutional Neural Network. In: 2017 IEEE International Conference on Image Processing (ICIP), pp 2055–2059
Li R, Li J, Wang X, Liang P, Gao J (2018) Detection of Gastric Cancer and its Histological Type based on Iodine Concentration in Spectral CT. Cancer Imaging 18(1):1–10
Li J, Li W, Sisk A, Ye H, Wallace W, Speier W, Arnold C (2020) A Multi-resolution Model for Histopathology Image Classification and Localization with Multiple Instance Learning. ar**v Preprint ar**v:2011.02679
Korkmaz S, Akçiçek A, Bínol H, Korkmaz M (2017) Recognition of the Stomach Cancer Images with Probabilistic HOG Feature Vector Histograms by Using HOG Features. In: 2017 IEEE 15th International Symposium on Intelligent Systems and Informatics (SISY), pages 000339–000342
Korkmaz S, Binol H (2018) Classification of molecular structure images by using ANN, RF, LBP HOG, and Size Reduction Methods for Early Stomach Cancer Detection. J Mol Struct 1156:255–263
Sharma H, Zerbe N, Klempert I, Lohmann S, Lindequist B, Hellwich O, Hufnagl P (2015) Appearance-based Necrosis Detection Using Textural Features and SVM with Discriminative Thresholding in Histopathological Whole Slide Images. In: 2015 IEEE 15th International Conference on Bioinformatics and Bioengineering (BIBE), pp 1–6
Liu B, Zhang M, Guo T, Cheng Y (2018) Classification of gastric slices based on deep learning and sparse representation. In: 2018 Chinese Control And Decision Conference (CCDC), pp 1825–1829
Sharma H, Zerbe N, Böger C., Wienert S, Hellwich O, Hufnagl P (2017) A Comparative Study of Cell Nuclei Attributed Relational Graphs for Knowledge Description and Categorization in Histopathological Gastric Cancer Whole Slide Images. In: 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS), pp 61–66
Sharma H, Zerbe N, Klempert I, Hellwich O, Hufnagl P (2017) Deep Convolutional Neural Networks for Automatic Classification of Gastric Carcinoma Using Whole Slide Images in Digital Histopathology. Comput Med Imaging Graph 61:2–13
Liu B, Yao K, Huang M, Zhang J, Li Y, Li R (2018) Gastric pathology image recognition based on deep residual networks. In: 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC), vol 2, pp 408–412
Wang S, Zhu Y, Yu L, Chen H, Lin H, Wan X, Fan X, Heng P (2019) RMDL: Recalibrated Multi-instance Deep Learning For Whole Slide Gastric Image Classification. Med Image Anal 101549:58
Song Z, Zou S, Zhou W, Huang Y, Shao L, Yuan J, Gou X, ** W, Wang Z, Chen X et al (2020) Clinically Applicable Histopathological Diagnosis System for Gastric Cancer Detection Using Deep Learning. Nat Commun 11(1):1–9
Kosaraju S, Hao J, Koh H, Kang M (2020) Deep-hipo: Multi-scale Receptive Field Deep Learning for Histopathological Image Analysis. Methods 179:3–13
Iizuka O, Kanavati F, Kato K, Rambeau M, Arihiro K, Tsuneki M (2020) Deep learning models for histopathological classification of gastric and colonic epithelial tumours. Scientific Reports 10(1):1–11
Ba J, Mnih V, Kavukcuoglu K (2014) Multiple Object Recognition with Visual Attention. ar**v preprint ar**v:1412.7755
Li W, Liu K, Zhang L, Cheng F (2020) Object detection based on an adaptive attention mechanism. Scientific Reports 10(1): 1–13
Xu K, Ba J, Kiros R, Cho K, Courville A, Salakhudinov R, Zemel R, Bengio Y (2015) Show, Attend and tell: neural image caption generation with visual attention. In: International conference on machine learning, pp 2048–2057
Liu M, Li L, Hu H, Guan W, Tian J (2020) Image Caption Generation with Dual Attention Mechanism. Information Processing & Management 57(2):102178
Sharma S, Kiros R, Salakhutdinov R (2015) Action recognition using visual attention. ar**v preprint ar**v:1511.04119
Chaudhari S, Polatkan G, Ramanath R, Mithal V (2019) An attentive survey of attention models. ar**v preprint ar**v:1904.02874
BenTaieb A, Hamarneh G (2018) Predicting cancer with a recurrent visual attention model for histopathology images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp 129–137
Li L, Xu M, Liu H, Li Y, Wang X, Jiang L, Wang Z, Fan X, Wang N (2019) A Large-Scale database and a CNN model for Attention-Based glaucoma detection. IEEE Trans Med Imaging 39 (2):413–424
Yang H, Kim J, Kim H, Adhikari S (2019) Guided soft attention network for classification of breast cancer histopathology images. IEEE Trans Med Imaging 39(5):1306–1315
Sun H, Zeng X, Xu T, Peng G, Ma Y (2019) Computer-aided Diagnosis in Histopathological Images of the Endometrium Using a Convolutional Neural Network and Attention Mechanisms. IEEE Journal of Biomedical and Health Informatics 24(6):1664–1676
Zhang X, Jiang Y, Peng H, Tu K, Goldwasser D (2017) Semi-Supervised Structured prediction with neural CRF autoencoder. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pp 1701–1711
Wicaksono A, Myaeng S (2013) Toward advice mining: conditional random fields for extracting Advice-Revealing text units. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp 2039– 2048
Zhuowen L, Wang K (2013) Human behavior recognition based on fractal conditional random field. In: 2013 25th Chinese Control and Decision Conference (CCDC), pp 1506–1510
Kruthiventi S, Babu R (2015) Crowd flow segmentation in compressed domain using CRF. In: 2015 IEEE International Conference on Image Processing (ICIP), pp 3417–3421
Liliana D, Basaruddin C (2017) A review on conditional random fields as a sequential classifier in machine learning. In: 2017 International Conference on Electrical Engineering and Computer Science (ICECOS), pp 143–148
Qu H, Wu P, Huang Q, Yi J, Riedlinger G, De S, Metaxas D (2019) Weakly supervised deep nuclei segmentation using points annotation in histopathology images. In: International Conference on Medical Imaging with Deep Learning, pp 390–400
Konstantinos Z, Henrik F, Raza S, Ioannis R, Yann J (2019) Y Yinyin. Superpixel-based Conditional Random Fields (superCRF): Incorporating Global and Local Context for Enhanced Deep Learning in Melanoma Histopathology. Frontiers in Oncology 9:1045
Li Y, Huang M, Zhang Y, Chen J, Xu H, Wang G, Feng W (2020) Automated Gleason Grading and Gleason Pattern Region Segmentation based on Deep Learning for Pathological Images of Prostate Cancer. IEEE Access 8:117714–117725
Dong J, Guo X, Wang G (2021) GECNN-CRF For prostate cancer detection with WSI. In: Proceedings of 2020 Chinese Intelligent Systems Conference, pp 646–658
Kosov S, Shirahama K, Li C, Grzegorzek M (2018) Environmental microorganism classification using conditional random fields and deep convolutional neural networks. Pattern Recogn 77:248– 261
Li C, Chen H, Zhang L, Xu N, Xue D, Hu Z, Ma H, Sun H (2019) Cervical Histopathology Image Classification Using Multilayer Hidden Conditional Random Fields and Weakly Supervised Learning. IEEE Access 7:90378–90397
Li Y, Wu X, Li C, Sun C, Li X, Rahaman M, Zhang H (2021) Intelligent Gastric Histopathology Image Classification Using Hierarchical Conditional Random Field based Attention Mechanism Inproceedings of the 2021 13th International Conference on Machine Learning and Computing
Li C, Li Y, Sun C, Chen H, Zhang H (2020) A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis. ar**v preprint ar**v:2009.13721
Lafferty J, McCallum A (2001) F pereira conditional random fields: probabilistic models for segmenting and labeling sequence data
Clifford P (1990) Markov random fields in statistics; disorder in physical systems: a volume in honour of John M Hammersley. Oxford University Press 19:32
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2018) Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4):834–848
Zheng S, Jayasumana S, Romera-Paredes B et al (2015) Conditional random fields as recurrent neural networks. In: Proc. of ICCV, vol 2015, pp 1–17
Gupta R (2006) Conditional random fields. Unpublished Report, IIT Bombay
Ronneberger O, Fischer P, Brox T (2016) U-net: Convolutional Networks for Biomedical Image Segmentation. In: Proc. ofMICCAI, vol 2015, pp 234–241
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 2818–2826
Simonyan K, Zisserman A (2014) Very Deep Convolutional Networks for Large-scale Image Recognition. ar**v:1409.1556
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 770–778
Kumar S, Hebert M (2006) Discriminative random fields. Int J Comput Vis 68(2):179–201
Kermany D, Goldbaum M, Cai W, Valentim C, Liang H, Baxter S, McKeown A, Yang G, Wu X, Yan F et al (2018) Identifying Medical Diagnoses and Treatable Diseases by Image-based Deep Learning. Cell 172(5):1122–1131
Deng S, Zhang X, Qin Y, Chen W, Fan H, Feng X, Wang J, Yan R, Zhao Y, Cheng Y et al (2020) miRNA-192 and-215 Activate Wnt/β-catenin Signaling Pathway in Gastric Cancer via APC. J Cell Physiol 235(9):6218–6229
Wang M, Yu Y, Liu F, Ren L, Zhang Q, Zou G (2018) Single Polydiacetylene Microtube Waveguide Platform for Discriminating microRNA-215 Expression Levels in Clinical Gastric Cancerous, Paracancerous and Normal Tissues. Talanta 188:27–34
Kamishima T, Hamasaki M, Akaho S (2009) Trbagg: A Simple Transfer Learning Method and its Application to Personalization in Collaborative Tagging. In: 2009 Ninth IEEE International Conference on Data Mining, pp 219–228
Deng J, Dong W, Socher R, Li L, Li K, Fei-Fei L (2009) Imagenet: A Large-scale Hierarchical Image Database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp 248–255
Kittler J, Hatef M, Duin R, Matas J (1998) On combining classifiers. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(3):226–239
Zhang Z, Lin C (2018) Pathological Image Classification of Gastric Cancer Based on Depth Learning. ACM Trans. Intell. Syst Technol. 45(11A):263–268
Fischer A, Jacobson K, Rose J, Zeller R (2008) Hematoxylin and Eosin Staining of Tissue and Cell Sections. Cold Spring Harbor Protocols 2008(5):pdb–prot4986
Miettinen M, Lasota J (2006) Gastrointestinal stromal tumors: review on morphology, molecular pathology, prognosis, and differential diagnosis. Archives of Pathology & Laboratory Medicine 130(10):1466–1478
Miettinen M (2003) Gastrointestinal Stromal Tumors (GISTs): Definition, Occurrence, Pathology, Differential Diagnosis and Molecular Genetics. Pol J Pathol, p 54
Zhang J, Li C, Kosov S, Grzegorzek M, Shirahama K, Jiang T, Sun C, Li Z, Li H (2021) Lcu-net: a novel low-cost u-net for environmental microorganism image segmentation. Pattern Recogn 107885:115
Fisher Y, Wang D, Shelhamer E, Darrell T (2018) Deep layer aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 2403–2412
Zhou Z, Siddiquee Md MR, Tajbakhsh N, Liang J (2018) Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pages 3–11. Springer
Chen L-C, Zhu Y, Papandreou G, Schroff F, Adam H (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European conference on computer vision (ECCV), pp 801–818
Chen L, Papandreou G, Kokkinos I, Murphy K, Yuille A (2017) Deeplab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence 40(4):834–848
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: A Deep Convolutional Encoder-decoder Architecture for Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(12):2481–2495
Osher S, Sethian J (1988) Fronts Propagating with Curvature-dependent speed: Algorithms Based on Hamilton-Jacobi Formulations. J Comput Phys 79(1):12–49
Otsu N (1979) Threshold Selection Method from Gray-level Histograms, A. IEEE Transactions on Systems, Man, and Cybernetics 9(1):62–66
Vincent L, Soille P (1991) Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(6):583–598
Li S (1994) Markov random field models in computer vision. In: Proc. of ECCV, vol 1994, pp 361–370
Kurmi Y, Chaurasia V (2020) Content-based Image Retrieval Algorithm for Nuclei Segmentation in Histopathology Images. Multimedia Tools and Applications, pp 1–21
Zafari S, Eerola T, Sampo J, Kälviäinen H, Haario H (2015) Segmentation of overlap** elliptical objects in silhouette images. IEEE Trans Image Process 24(12):5942–5952
Wang Z (2016) A Semi-automatic Method for Robust and Efficient Identification of Neighboring Muscle Cells. Pattern Recogn 53:300–312
Lei T, Jia X, Zhang Y, He L, Meng H, Nandi A (2018) Significantly Fast and Robust Fuzzy c-means Clustering Algorithm Based on Morphological Reconstruction and Membership Filtering. IEEE Trans Fuzzy Syst 26(5):3027–3041
Vu Q, Graham S, Kurc T, To M, Shaban M, Qaiser T, Koohbanani N, Khurram S, Kalpathy-Cramer J, Zhao T et al (2019) Methods for Segmentation and Classification of Digital Microscopy Tissue Images. Frontiers in Bioengineering and Biotechnology 7:53
Peng Y, Liu S, Qiang Y, Wu X, Hong L (2019) A local mean and variance active contour model for biomedical image segmentation. Journal of Computational Science 33:11–19
Yu C, Yan Y, Zhao S, Zhang Y (2020) Pyramid Feature Adaptation for Semi-supervised Cardiac Bi-ventricle Segmentation. Comput Med Imaging Graph 101697:81
Sheela C, Suganthi G (2020) morphological edge detection and brain tumor segmentation in magnetic resonance (MR) images based on region growing and performance evaluation of modified fuzzy C-Means (FCM) algorithm. Multimedia Tools and Applications, pp 1–14
Kingma D, Ba J (2014) Adam:, A method for stochastic optimization. ar**v preprint ar**v:1412.6980
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An image is worth 16x16 words:, Transformers for image recognition at scale. ar**v preprint ar**v:2010.11929
Hu J, Shen L, Sun G (2018) Squeeze-and-excitation Networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7132–7141
Woo S, Park J, Lee J, Kweon I (2018) Cbam: convolutional block attention module. In: Proceedings of the European Conference on Computer Vision (ECCV), pp 3–19
Wang X, Girshick R, Gupta A, He K (2018) Non-local Neural Networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 7794–7803
Cao Y, Xu J, Lin S, Wei F, Hu H (2019) Gcnet: Non-local Networks Meet Squeeze-excitation Networks and Beyond. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops, pp 0–0
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp 448–456
Hammad M, Pławiak P, Wang K, Acharya U (2020) ResNet-Attention Model for Human Authentication Using ECG Signals. Expert Systems, p e12547
Roy S, Manna S, Song T, Bruzzone L (2020) Attention-Based Adaptive Spectral-Spatial Kernel ResNet for Hyperspectral Image Classification. IEEE Transactions on Geoscience and Remote Sensing
Marszalek M, Schmid C (2007) Accurate object localization with shape masks. In: IEEE Conference On Computer Vision and Pattern Recognition, pages 1–8. IEEE, p 2007
Parkhi OM, Vedaldi A, Zisserman A, Jawahar CV (2012) Cats and dogs. In: IEEE Conference on Computer Vision and Pattern Recognition
Mishkin D, Matas J (2015) All You Need is a Good Init. ar**v preprint ar**v:1511.06422
Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, Dehghani M, Minderer M, Heigold G, Gelly S et al (2020) An Image is Worth 16x16 Words:, Transformers for Image Recognition at Scale. ar**v preprint ar**v:2010.11929
Acknowledgements
This study was supported by the National Natural Science Foundation of China (grant No. 61806047). We thank Miss **ran Wu, due to her contribution is considered as important as the first author in this paper. We thank M.D. **aoyan Li, due to her contribution is considered as important as the corresoponding author in this paper. We thank Miss Zixian Li and Mr. Guoxian Li for their important discussion. We also thank B.E. Haoyuan Chen for his help in the experiments.
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Li, Y., Wu, X., Li, C. et al. A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification. Appl Intell 52, 9717–9738 (2022). https://doi.org/10.1007/s10489-021-02886-2
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DOI: https://doi.org/10.1007/s10489-021-02886-2