Introduction to digital pathology and computer-aided pathology

Nam, Soojeong; Chong, Yosep; Jung, Chan Kwon; Kwak, Tae-Yeong; Lee, Ji Youl; Park, Jihwan; Rho, Mi Jung; Go, Heounjeong

doi:10.4132/jptm.2019.12.31

Cited by 77 publications

(52 citation statements)

References 60 publications

(61 reference statements)

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“…The technical innovations in the past decade have advanced DP enough for it to replace conventional microscopic diagnosis [1]. However, caution is still needed in certain situations that require specific pathologic determination, such as microbial infection assessment [1]. Ultimately, accumulating experience and data could lead to solutions to these current technical limitations.…”

Section: Resultsmentioning

confidence: 99%

“…When diagnoses are made using portable terminals such as a tablet PC, the use of a relatively small screen is a major concern. The results of a recent study suggest that diagnosis using portable terminals should be limited to special cases that require relatively low accuracy but rapid reporting of results, such as frozen section tests [1,53,65]. These terminals are generally not reliable for routine diagnostic work.…”

Section: Issues Related To Telepathology Firewalls Protection Of Pementioning

confidence: 99%

“…Digital pathology (DP) refers to the use of a digital scanner to convert and save pathologic slides as digital images and the use of those images for pathologic diagnosis [1,2]. DP includes various processes of pathologic diagnosis, including primary diagnosis based on whole slide imaging (WSI) that enables diagnosis through display devices such as monitors instead of a microscope; telepathology that allows opinions to be obtained and digital images to be shared with experts at different locations over a network connection; and computer-aided diagnosis based on quantitative and morphometric analyses made using image analysis software [1].…”

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confidence: 99%

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Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Chong

Kim

Jung

et al. 2020

J Pathol Transl Med

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Digital pathology (DP) using whole slide imaging (WSI) is becoming a fundamental issue in pathology with recent advances and the rapid development of associated technologies. However, the available evidence on its diagnostic uses and practical advice for pathologists on implementing DP remains insufficient, particularly in light of the exponential growth of this industry. To inform DP implementation in Korea, we developed relevant and timely recommendations. We first performed a literature review of DP guidelines, recommendations, and position papers from major countries, as well as a review of relevant studies validating WSI. Based on that information, we prepared a draft. After several revisions, we released this draft to the public and the members of the Korean Society of Pathologists through our homepage and held an open forum for interested parties. Through that process, this final manuscript has been prepared. This recommendation contains an overview describing the background, objectives, scope of application, and basic terminology; guidelines and considerations for the hardware and software used in DP systems and the validation required for DP implementation; conclusions; and references and appendices, including literature on DP from major countries and WSI validation studies.

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Section: Resultsmentioning

confidence: 99%

Section: Issues Related To Telepathology Firewalls Protection Of Pementioning

confidence: 99%

mentioning

confidence: 99%

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Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Chong

Kim

Jung

et al. 2020

J Pathol Transl Med

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“…In recent years, we have witnessed the rapid development of deep learning techniques, which have been widely used for medical imaging and achieved impressive performances in various tasks such as lung nodule detection in CT and pathological image classification [16,17]. At present, great progress has been made in developing AI-guided digital colposcopy to improve the efficiency and accuracy of clinical diagnosis.…”

Section: The Advancements In Computer Algorithms Applying To Cervicalmentioning

confidence: 99%

The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence

Xue

Qiao

2020

BMC Med

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Background: The World Health Organization (WHO) called for global action towards the elimination of cervical cancer. One of the main strategies is to screen 70% of women at the age between 35 and 45 years and 90% of women managed appropriately by 2030. So far, approximately 85% of cervical cancers occur in low-and middleincome countries (LMICs). The colposcopy-guided biopsy is crucial for detecting cervical intraepithelial neoplasia (CIN) and becomes the main bottleneck limiting screening performance. Unprecedented advances in artificial intelligence (AI) enable the synergy of deep learning and digital colposcopy, which offers opportunities for automatic image-based diagnosis. To this end, we discuss the main challenges of traditional colposcopy and the solutions applying AI-guided digital colposcopy as an auxiliary diagnostic tool in low-and middle-income countries (LMICs). Main body: Existing challenges for the application of colposcopy in LMICs include strong dependence on the subjective experience of operators, substantial inter-and intra-operator variabilities, shortage of experienced colposcopists, consummate colposcopy training courses, and uniform diagnostic standard and strict quality control that are hard to be followed by colposcopists with limited diagnostic ability, resulting in discrepant reporting and documentation of colposcopy impressions. Organized colposcopy training courses should be viewed as an effective way to enhance the diagnostic ability of colposcopists, but implementing these courses in practice may not always be feasible to improve the overall diagnostic performance in a short period of time. Fortunately, AI has the potential to address colposcopic bottleneck, which could assist colposcopists in colposcopy imaging judgment, detection of underlying CINs, and guidance of biopsy sites. The automated workflow of colposcopy examination could create a novel cervical cancer screening model, reduce potentially false negatives and false positives, and improve the accuracy of colposcopy diagnosis and cervical biopsy. Conclusion: We believe that a practical and accurate AI-guided digital colposcopy has the potential to strengthen the diagnostic ability in guiding cervical biopsy, thereby improves cervical cancer screening performance in LMICs and accelerates the process of global cervical cancer elimination eventually.

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“…Nam et al provide an introduction to digital pathology aimed at healthcare professionals. 25 Furthermore, Komura and Ishikawa made a short review of machine learning methods for histopathological image analysis, listing seven whole slide image datasets and 21 hand annotated histopathological datasets. 16…”

Section: Short Introduction To Whole Slide Images In Cancer Diagnosismentioning

confidence: 99%

Model Fooling Attacks Against Medical Imaging: A Short Survey

Sipola¹,

Puuska²,

Kokkonen³

2020

ISIJ

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This study aims to find a list of methods to fool artificial neural networks used in medical imaging. We collected a short list of publications related to machine learning model fooling to see if these methods have been used in the medical imaging domain. Specifically, we focused our interest to pathological whole slide images used to study human tissues. While useful, machine learning models such as deep neural networks can be fooled by quite simple attacks involving purposefully engineered images. Such attacks pose a threat to many domains, including the one we focused on since there have been some studies describing such threats.

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Introduction to digital pathology and computer-aided pathology

Cited by 77 publications

References 60 publications

Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

Recommendations for pathologic practice using digital pathology: consensus report of the Korean Society of Pathologists

The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence

Model Fooling Attacks Against Medical Imaging: A Short Survey

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