Deep multiple-instance learning for abnormal cell detection in cervical histopathology images

Pal, Anupam; Xue, Zhiyun; Desai, Kanan T.; Banjo, Adekunbiola Aina; Adepiti, Clement A.; Long, L. Rodney; Schiffman, Mark

doi:10.1016/j.compbiomed.2021.104890

Cited by 35 publications

(5 citation statements)

References 62 publications

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“…Computer-assisted diagnosis is believed to ease this situation because it can potentially lower the misdiagnosis rate and also reduce the workload of cytologists [ 100 ]. Therefore, several studies have addressed the subject of automatic cervical cancer diagnosis [ 64 , 65 , 66 , 67 , 68 , 74 , 75 , 80 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 ]. The investigations showed that AI-assisted methods were promising, and achieved a high sensitivity and specificity in clinical cervical cytological screening [ 66 ,…”

Section: Resultsmentioning

confidence: 99%

“…In fact, reading with AI assistance was approximately 380 times faster than reading by a typical pathologist. AI algorithms were able to distinguish between normal and cancerous Pap smears with an accuracy of 80–100% [ 68 , 110 , 111 , 113 , 115 , 116 , 119 , 120 , 125 , 127 , 130 , 131 , 135 , 137 ]. The application of AI in cytology for the detection of cervical cancer is shown in Table 3 .…”

Section: Resultsmentioning

confidence: 99%

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Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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Objective: The likelihood of timely treatment for cervical cancer increases with timely detection of abnormal cervical cells. Automated methods of detecting abnormal cervical cells were established because manual identification requires skilled pathologists and is time consuming and prone to error. The purpose of this systematic review is to evaluate the diagnostic performance of artificial intelligence (AI) technologies for the prediction, screening, and diagnosis of cervical cancer and pre-cancerous lesions. Materials and Methods: Comprehensive searches were performed on three databases: Medline, Web of Science Core Collection (Indexes = SCI-EXPANDED, SSCI, A & HCI Timespan) and Scopus to find papers published until July 2022. Articles that applied any AI technique for the prediction, screening, and diagnosis of cervical cancer were included in the review. No time restriction was applied. Articles were searched, screened, incorporated, and analyzed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-analyses guidelines. Results: The primary search yielded 2538 articles. After screening and evaluation of eligibility, 117 studies were incorporated in the review. AI techniques were found to play a significant role in screening systems for pre-cancerous and cancerous cervical lesions. The accuracy of the algorithms in predicting cervical cancer varied from 70% to 100%. AI techniques make a distinction between cancerous and normal Pap smears with 80–100% accuracy. AI is expected to serve as a practical tool for doctors in making accurate clinical diagnoses. The reported sensitivity and specificity of AI in colposcopy for the detection of CIN2+ were 71.9–98.22% and 51.8–96.2%, respectively. Conclusion: The present review highlights the acceptable performance of AI systems in the prediction, screening, or detection of cervical cancer and pre-cancerous lesions, especially when faced with a paucity of specialized centers or medical resources. In combination with human evaluation, AI could serve as a helpful tool in the interpretation of cervical smears or images.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

et al. 2022

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“… 437 As labels at the WSI level are much easier to obtain (and hence more prevalent) than patch-level annotations, MIL has been applied to CPath by a significant number of papers. 62 , 63 , 64 , 267 , 268 , 308 , 316 , 418 , 437 , 438 , 439 , 440 , 441 , 442 , 443 , 444 , 445 , 446 , 447 , 448 , 449 , 450 , 451 , 452 , 453 , 454 , 455 , 456 Since both utilize coarser annotations for training on massive images, MIL is similar to weakly-supervised learning. However, weak supervision predicts at a finer level (e.g.…”

Section: Model Learning For Cpathmentioning

confidence: 99%

Computational Pathology: A Survey Review and The Way Forward

Hosseini,

Bejnordi,

Trinh

et al. 2024

Journal of Pathology Informatics

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“…Additionally, many attention-based RNN model variants have arisen in the NLP community. The widespread adoption of the attention mechanism in fields as diverse as multimedia recommendation 30 and medical diagnosis 31 demonstrates its versatility and utility in a wide range of machine-learning contexts. Human pose estimation using stacked hourglass networks for feature extraction was planned by Chun et al 32 .…”

Section: Literature Surveymentioning

confidence: 99%

Attention-based bidirectional-long short-term memory for abnormal human activity detection

Kumar,

Patel,

Biswas

et al. 2023

Sci Rep

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Abnormal human behavior must be monitored and controlled in today’s technology-driven era, since it may cause damage to society in the form of assault or web-based violence, such as direct harm to a person or the propagation of hate crimes through the internet. Several authors have attempted to address this issue, but no one has yet come up with a solution that is both practical and workable. Recently, deep learning models have become popular as a means of handling massive amounts of data but their potential to categorize the aberrant human activity remains unexplored. Using a convolutional neural network (CNN), a bidirectional long short-term memory (Bi-LSTM), and an attention mechanism to pay attention to the unique spatiotemporal characteristics of raw video streams, a deep-learning approach has been implemented in the proposed framework to detect anomalous human activity. After analyzing the video, our suggested architecture can reliably assign an abnormal human behavior to its designated category. Analytic findings comparing the suggested architecture to state-of-the-art algorithms reveal an accuracy of 98.9%, 96.04%, and 61.04% using the UCF11, UCF50, and subUCF crime datasets, respectively.

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Deep multiple-instance learning for abnormal cell detection in cervical histopathology images

Cited by 35 publications

References 62 publications

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Diagnosis of Cervical Cancer and Pre-Cancerous Lesions by Artificial Intelligence: A Systematic Review

Computational Pathology: A Survey Review and The Way Forward

Attention-based bidirectional-long short-term memory for abnormal human activity detection

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