Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images

Abdolahi, Mohammad; Salehi, Mahdi; Shokatian, Iman; Reiazi, Reza

doi:10.47176/mjiri.34.140

Cited by 18 publications

(14 citation statements)

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“…Many professionals are involved in the introduction of AI in DP, ranging from the bioengineer to the pathologist up to the biomedical laboratory technician. There is also no doubt that AI could represent a serious opportunity for the DP laboratories [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 ]. It is, however, the time to investigate the full introduction in the routine.…”

Section: Discussionmentioning

confidence: 99%

“…We dedicated particular attention to the critical issues, given that AI will rely heavily on it. In the same study, we highlighted the opportunities and the challenges of AI according to the most recent studies [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 ]. Some important development guidelines have been identified.…”

Section: Introductionmentioning

confidence: 96%

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Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

et al. 2021

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Motivation: This study deals with the introduction of artificial intelligence (AI) in digital pathology (DP). The study starts from the highlights of a companion paper. Objective: The aim was to investigate the consensus and acceptance of the insiders on this issue. Procedure: An electronic survey based on the standardized package Microsoft Forms (Microsoft, Redmond, WA, USA) was proposed to a sample of biomedical laboratory technicians (149 admitted in the study, 76 males, 3 females, mean age 44.2 years). Results: The survey showed no criticality. It highlighted (a) the good perception of the basic training on both groups, and (b) a uniformly low perceived knowledge of AI (as arisen from the graded questions). Expectations, perceived general impact, perceived changes in the work-flow, and worries clearly emerged in the study. Conclusions: The of AI in DP is an unstoppable process, as well as the increase of the digitalization in the health domain. Stakeholders must not look with suspicion towards AI, which can represent an important resource, but should invest in monitoring and consensus training initiatives based also on electronic surveys.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 96%

Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

et al. 2021

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“…A quick look at PubMed with the following search key: (digital pathology [Title]) AND (artificial intelligence [Title]) currently reports 17 works [6,[13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28].…”

Section: Towards the Revolution Of The Digital Pathology And Artificial Intelligence 41 What Is Emerging In The Application Of The Artifimentioning

confidence: 99%

“…There is currently optimism about the opportunities of ANNs, as has been highlighted above in the studies [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28]. There are several difficulties with deploying AI tools in practice depending on the expectance and the objectives of the pathologist.…”

Section: Realism Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Giovagnoli

Giansanti

2021

Healthcare

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This commentary aims to address the field of Artificial intelligence (AI) in Digital Pathology (DP) both in terms of the global situation and research perspectives. It has four polarities. First, it revisits the evolutions of digital pathology with particular care to the two fields of the digital cytology and the digital histology. Second, it illustrates the main fields in the employment of AI in DP. Third, it looks at the future directions of the research challenges from both a clinical and technological point of view. Fourth, it discusses the transversal problems among these challenges and implications and introduces the immediate work to implement.

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“…In the first method of Abdolahi et al. [34] a simple convolutional neural network (named baseline model) has been used and in the second approach a pre‐trained VGG‐16 CNN model via feature extraction and fine‐tuning for the classification of breast pathology images has been utilised. Lin et al.…”

Section: Introductionmentioning

confidence: 99%

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

Jafarbigloo

Danyali

2021

CAAI Trans on Intel Tech

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Early diagnosis of breast cancer, the most common disease among women around the world, increases the chance of treatment and is highly important. Nuclear atypia grading in histopathological images plays an important role in the final diagnosis and grading of breast cancer. Grading images by pathologists is a time consuming and subjective task. Therefore, the existence of a computer-aided system for nuclear atypia grading is very useful and necessary. In this study, two automatic systems for grading nuclear atypia in breast cancer histopathological images based on deep learning methods are proposed. A patch-based approach is introduced due to the large size of the histopathological images and restriction of the training data. In the proposed system I, the most important patches in the image are detected first and then a three-hidden-layer convolutional neural network (CNN) is designed and trained for feature extraction and to classify the patches individually. The proposed system II is based on a combination of the CNN for feature extraction and a twolayer Long short-term memory (LSTM) network for classification. The LSTM network is utilised to consider all patches of an image simultaneously for image grading. The simulation results show the efficiency of the proposed systems for automatic nuclear atypia grading and outperform the current related studies in the literature. K E Y W O R D S breast cancer, CNN, histopathological image, LSTM networks, nuclear atypia | INTRODUCTIONBreast cancer is caused due to an outgrowth of abnormal cells in the breast. This disease is the second leading cause of death among women around the world [1] and is a serious problem for most human societies. Early diagnosis of this disease increases the chance of efficient treatment. After performing mammography and ultrasound imaging, if abnormal tissues are found, a biopsy examination is required. In this test, a small sample of the breast tissue is placed under a microscope to acquire histopathological images for further investigation. Histopathology refers to the visual examination of tissue [2]. Histopathological images are available in digital format which is analysed to detect the type and grade of cancer [3]. Various magnifications of histological images are available such as 10X, 20X and 40X.Traditional methods for grading breast cancer are manually performed by pathologists. Pathologists examine histologicalThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Artificial intelligence in automatic classification of invasive ductal carcinoma breast cancer in digital pathology images

Cited by 18 publications

References 0 publications

Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

Artificial Intelligence in Digital Pathology: What Is the Future? Part 2: An Investigation on the Insiders

Artificial Intelligence in Digital Pathology: What Is the Future? Part 1: From the Digital Slide Onwards

Nuclear atypia grading in breast cancer histopathological images based on CNN feature extraction and LSTM classification

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