Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks

Sun, Yibao; Xu, Zhaoyang; Strell, Carina; Moro, Carlos Fernández; Wärnberg, Fredrik; Dong, Le; Zhang, Qianni

doi:10.1109/ipas.2018.8708869

Cited by 10 publications

(8 citation statements)

References 11 publications

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“…Besides BreaKHis, other images were also applied. All of these breast cancer databases have similar characteristics, for example, using the Wisconsin Original Dataset [ 10 ], the University of Michigan and University of British Columbia Virtual Slidebox [ 19 ] or other individual databases [ 20 , 21 , 22 ]. The different magnifications of images and the multi-resolution databases are essential to make the models more robust and generalizable [ 23 ].…”

Section: Discussionmentioning

confidence: 99%

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

et al. 2022

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In this paper, we propose a novel approach to segment tumor and normal regions in human breast tissues. Cancer is the second most common cause of death in our society; every eighth woman will be diagnosed with breast cancer in her life. Histological diagnosis is key in the process where oncotherapy is administered. Due to the time-consuming analysis and the lack of specialists alike, obtaining a timely diagnosis is often a difficult process in healthcare institutions, so there is an urgent need for improvement in diagnostics. To reduce costs and speed up the process, an automated algorithm could aid routine diagnostics. We propose an area-based annotation approach generalized by a new rule template to accurately solve high-resolution biological segmentation tasks in a time-efficient way. These algorithm and implementation rules provide an alternative solution for pathologists to make decisions as accurate as manually. This research is based on an individual database from Semmelweis University, containing 291 high-resolution, bright field microscopy breast tumor tissue images. A total of 70% of the 128 × 128-pixel resolution images (206,174 patches) were used for training a convolutional neural network to learn the features of normal and tumor tissue samples. The evaluation of the small regions results in high-resolution histopathological image segmentation; the optimal parameters were calculated on the validation dataset (29 images, 10%), considering the accuracy and time factor as well. The algorithm was tested on the test dataset (61 images, 20%), reaching a 99.10% f1 score on pixel level evaluation within 3 min on average. Besides the quantitative analyses, the system’s accuracy was measured qualitatively by a histopathologist, who confirmed that the algorithm was also accurate in regions not annotated before.

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Section: Discussionmentioning

confidence: 99%

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

et al. 2022

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“…The accuracy attained with this model was 73.68% percent. Additional work was proposed in [13]. The authors presented a method with the goal of lowering the death rate from breast cancer and saving women's lives.…”

Section: Pathology Imagesmentioning

confidence: 99%

“…The worrisome lesion observed on the microscope biopsy images is detected using this method.These lesions are to be classified into Benign, Breast Cancer Invasive Ductal Carcinoma, or Ductal Carcinoma In Situ. To automate a system like our suggested approach, there have been a number of attempts and trial [13]. The paper's most significant contribution is as follows.…”

Section: Introductionmentioning

confidence: 99%

The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Mohamed

Amer²,

Eldin

et al. 2022

Journal of Computing and Communication

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According to the World Health Organization, cancer is the second leading cause of mortality. Breast cancer is the most prevalent cancer diagnosed in women around the world. Breast cancer diagnostics range from mammograms to CT scans and ultrasounds, but a biopsy is the only way to know for sure if the suspicious cells detected in the breast are cancerous or not. This paper's main contribution is multi-fold. First, it proposes a deep learning approach to detect breast cancer from biopsy microscopy images. Deep convolution nets of various types are used. Second, the paper examines the effects of different data preprocessing techniques on the performance of deep learning models. Third, the paper introduces an ensemble method for aggregating the best models in order to improve performance. The experimental results revealed that Densenet169, Resnet50, and Resnet101 are the three best models achieving accuracy scores of 62%, 68%, and 85%, respectively. without data preprocessing. With the help of data augmentation and segmentation, the accuracy of these models increased by 20%, 17%, and 6%, respectively. Additionally, the ensemble learning technique improves the accuracy of the models even further. The results show that the best accuracy achieved is 92.5%.

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“…The GoogLeNet-based architecture produced a test accuracy of 85% among different algorithms. Sun et al [ 36 ] used a probability map to delineate the tumor border using CNN trained from small patches cropped from histology images. Thagaard [ 37 ] presented an algorithm which can automatically detect cancer and classify WSI into metastasis subtypes in the Camelyon17 challenge which focused on patient-level analysis.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Johny

2021

Computational and Mathematical Methods in Medicine

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Diagnosis of different breast cancer stages using histopathology whole slide images (WSI) is the gold standard in determining the grade of tissue metastasis. Computer-aided diagnosis (CAD) assists medical experts as a second opinion tool in early detection to prevent further proliferation. The field of pathology has advanced so rapidly that it is possible to obtain high-quality images from glass slides. Patches from the region of interest in histopathology images are extracted and trained using artificial neural network models. The trained model primarily analyzes and predicts the histology images for the benign or malignant class to which it belongs. Classification of medical images focuses on the training of models with layers of abstraction to distinguish between these two classes with less false-positive rates. The learning rate is the crucial hyperparameter used during the training of deep convolutional neural networks (DCNN) to improve model accuracy. This work emphasizes the relevance of the dynamic learning rate than the fixed learning rate during the training of networks. The dynamic learning rate varies with preset conditions between the lower and upper boundaries and repeats at different iterations. The performance of the model thus improves and attains comparatively high accuracy with fewer iterations.

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Detection of Breast Tumour Tissue Regions in Histopathological Images using Convolutional Neural Networks

Cited by 10 publications

References 11 publications

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

Breast Tumor Tissue Segmentation with Area-Based Annotation Using Convolutional Neural Network

The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning

Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

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