Breast Cancer Histopathological Image Classification: A Deep Learning Approach

Jannesari, Mahboubeh; Habibzadeh, Mehdi; Aboulkheyr, HamidReza; Khosravi, Pegah; Elemento, Olivier; Totonchi, Mehdi; Hajirasouliha, Iman

doi:10.1109/bibm.2018.8621307

Cited by 65 publications

(27 citation statements)

References 26 publications

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“…Comparison with related research. We compared our methods with seven existing CRC detection methods 13,17,35,[38][39][40][41] , and five other cancers (lung, ductal carcinoma, breast, prostate, basal cell carcinoma) detection methods 7,[42][43][44][45] (Supplementary Table 2). The 6 of 7 CRC detection methods had an AUC ranging from 0.904 to 0.99 based on SL.…”

Section: Resultsmentioning

confidence: 99%

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Sun

et al. 2021

Nat Commun

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Machine-assisted pathological recognition has been focused on supervised learning (SL) that suffers from a significant annotation bottleneck. We propose a semi-supervised learning (SSL) method based on the mean teacher architecture using 13,111 whole slide images of colorectal cancer from 8803 subjects from 13 independent centers. SSL (~3150 labeled, ~40,950 unlabeled; ~6300 labeled, ~37,800 unlabeled patches) performs significantly better than the SL. No significant difference is found between SSL (~6300 labeled, ~37,800 unlabeled) and SL (~44,100 labeled) at patch-level diagnoses (area under the curve (AUC): 0.980 ± 0.014 vs. 0.987 ± 0.008, P value = 0.134) and patient-level diagnoses (AUC: 0.974 ± 0.013 vs. 0.980 ± 0.010, P value = 0.117), which is close to human pathologists (average AUC: 0.969). The evaluation on 15,000 lung and 294,912 lymph node images also confirm SSL can achieve similar performance as that of SL with massive annotations. SSL dramatically reduces the annotations, which has great potential to effectively build expert-level pathological artificial intelligence platforms in practice.

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Section: Resultsmentioning

confidence: 99%

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Sun

et al. 2021

Nat Commun

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“…Using another independent set of 10,116 (49.92%) cancer and 10,148 (50.08%) non-cancer patches, the AI for patch-level prediction achieved a testing accuracy of 98.11% and an AUC of 99.83%. The AUC outperformed that of all the previous AI studies for CRC diagnosis and prediction (79.2–99.4%) and even for the majority of other types of cancer (82.9–99.9%, see Additional file 1 : Supplementary-Tables 3, [ 8 , 12 , 17 , 19 , 22 , 43 – 48 ]). The specificity was 99.22% and the sensitivity 96.99%, both outstanding.…”

Section: Resultsmentioning

confidence: 72%

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

Wang¹,

Yu²,

Xu³

et al. 2021

BMC Med

107

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Background Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany. Results Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells. Conclusions This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

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“…For cancer diagnosis, CNN-based DL models have exhibited exceptional accuracy in identifying malignant tumors using histopathology slides [30][31][32][33][34][35]. In an international competition (CAMELYON16) for diagnosing breast cancer metastasis in lymph nodes using WSI with hematoxylin-eosin (HE) staining, the best CNN algorithm (a GoogLeNet architecture-based model) yielded an AUC of 0.994, outperforming the best pathologist with an AUC of 0.884 and in a more time-efficient manner [33].…”

Section: Cancer Diagnosis Classification and Gradingmentioning

confidence: 99%

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Chen

Lin

et al. 2021

Cancer Communications

124

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Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.

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Breast Cancer Histopathological Image Classification: A Deep Learning Approach

Cited by 65 publications

References 26 publications

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

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