Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion

George, Kalpana; Faziludeen, Shameer; Sankaran, Praveen; K, Paul Joseph

doi:10.1016/j.compbiomed.2020.103954

Cited by 54 publications

(32 citation statements)

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“…Most of these works attempted to manually extract textural, morphological, shape, and other correlated features from the image [18], [19], [20], and used some traditional classifiers, such as support vector machine (SVM) and random forest to perform classification [21], [22]. Some researchers proposed nuclear analysis-based methods [23], [24]. It is worth noting that the above methods were carried out on very small datasets.…”

Section: Related Workmentioning

confidence: 99%

Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

et al. 2021

IEEE Access

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Deep learning approaches have demonstrated significant progress in breast cancer histopathological image diagnosis. Training an interpretable diagnosis model using high-resolution histopathological image is still challenging. To alleviate this problem, a novel multi-view attention-guided multiple instance detection network (MA-MIDN) is proposed. The traditional image classification problem is framed as a weakly supervised multiple instance learning (MIL) problem. We first divide each histopathology image into instances and form a corresponding bag to fully utilize high-resolution information through MIL. Then a new multiple-view attention (MVA) algorithm is proposed to learn attention on the instances from the image to localize the lesion regions in this image. A MVA-guided MIL pooling strategy is designed for aggregating instance-level features to obtain bag-level features for the final classification. The proposed MA-MIDN model performs lesion localization and image classification, simultaneously. Particularly, we train the MA-MIDN model under the deep mutual learning (DML) schema. This transfers DML to a weakly supervised learning problem. Three public breast cancer histopathological image datasets are chosen to evaluate classification and localization results. The experimental results demonstrate that the MA-MIDN model is superior to the latest baselines in terms of diagnosis accuracy, AUC, Precision, Recall, and F1. Notably, it achieves better localization results without compromising classification performance, thereby proving its higher practicality. The code for the MA-MIDN model is available at https://github.com/lcxlcx/MA-MIDN.INDEX TERMS breast cancer diagnosis; multiple instance learning; multi-view attention; diagnosis interpretability; deep mutual learning.

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Section: Related Workmentioning

confidence: 99%

Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

et al. 2021

IEEE Access

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“…George et. al [ 174 ] proposed a nucleus-guided transfer learning approach for BCHI. The features were extracted using CNN pre-trained on ImageNet.…”

Section: Image Processing Approachesmentioning

confidence: 99%

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

2021

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Breast cancer in women is the second most common cancer worldwide. Early detection of breast cancer can reduce the risk of human life. Non-invasive techniques such as mammograms and ultrasound imaging are popularly used to detect the tumour. However, histopathological analysis is necessary to determine the malignancy of the tumour as it analyses the image at the cellular level. Manual analysis of these slides is time consuming, tedious, subjective and are susceptible to human errors. Also, at times the interpretation of these images are inconsistent between laboratories. Hence, a Computer-Aided Diagnostic system that can act as a decision support system is need of the hour. Moreover, recent developments in computational power and memory capacity led to the application of computer tools and medical image processing techniques to process and analyze breast cancer histopathological images. This review paper summarizes various traditional and deep learning based methods developed to analyze breast cancer histopathological images. Initially, the characteristics of breast cancer histopathological images are discussed. A detailed discussion on the various potential regions of interest is presented which is crucial for the development of Computer-Aided Diagnostic systems. We summarize the recent trends and choices made during the selection of medical image processing techniques. Finally, a detailed discussion on the various challenges involved in the analysis of BCHI is presented along with the future scope.

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“…First-generation AI systems developed over the last decade have largely focused on clinical decision making through big data analysis as a way to generate evidence-based information. These include supervised machine learning, where the algorithm based on a labeled dataset, provides a model that the algorithm can use to determine its accuracy on training data ( 16 ); An unsupervised modeling which provides unlabeled data that the algorithm attempts to fit by extracting patterns on its own ( 17 ); recommender systems that seeks to predict the preference a user would give to an item ( 18 ); expert systems which attempt to match the decision-making ability of a human expert ( 19 ); natural language processing which process and analyze large amounts of natural language data ( 20 ); computer vision which analyzes data from digital images or videos ( 21 ); and expert-guided feature extraction schemes, where derived features are based on an initial set of measured data for facilitating subsequent learning and generalization steps ( 22 , 23 ).…”

Section: Introductionmentioning

confidence: 99%

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

Ilan

2020

Front. Digit. Health

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Artificial intelligence (AI) digital health systems have drawn much attention over the last decade. However, their implementation into medical practice occurs at a much slower pace than expected. This paper reviews some of the achievements of first-generation AI systems, and the barriers facing their implementation into medical practice. The development of second-generation AI systems is discussed with a focus on overcoming some of these obstacles. Second-generation systems are aimed at focusing on a single subject and on improving patients' clinical outcomes. A personalized closed-loop system designed to improve end-organ function and the patient's response to chronic therapies is presented. The system introduces a platform which implements a personalized therapeutic regimen and introduces quantifiable individualized-variability patterns into its algorithm. The platform is designed to achieve a clinically meaningful endpoint by ensuring that chronic therapies will have sustainable effect while overcoming compensatory mechanisms associated with disease progression and drug resistance. Second-generation systems are expected to assist patients and providers in adopting and implementing of these systems into everyday care.

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Breast cancer detection from biopsy images using nucleus guided transfer learning and belief based fusion

Cited by 54 publications

References 36 publications

Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis

Breast histopathological image analysis using image processing techniques for diagnostic purposes: A methodological review

Second-Generation Digital Health Platforms: Placing the Patient at the Center and Focusing on Clinical Outcomes

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