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PloS One 2021Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide...
Pathologists generally pan, focus, zoom and scan tissue biopsies either under microscopes or on digital images for diagnosis. With the rapid development of whole-slide digital scanners for histopathology, computer-assisted digital pathology image analysis has attracted increasing clinical attention. Thus, the working style of pathologists is also beginning to change. Computer-assisted image analysis systems have been developed to help pathologists perform basic examinations. This paper presents a novel lightweight detection framework for automatic tumor detection in whole-slide histopathology images. We develop the Double Magnification Combination (DMC) classifier, which is a modified DenseNet-40 to make patch-level predictions with only 0.3 million parameters. To improve the detection performance of multiple instances, we propose an improved adaptive sampling method with superpixel segmentation and introduce a new heuristic factor, local sampling density, as the convergence condition of iterations. In postprocessing, we use a CNN model with 4 convolutional layers to regulate the patch-level predictions based on the predictions of adjacent sampling points and use linear interpolation to generate a tumor probability heatmap. The entire framework was trained and validated using the dataset from the Camelyon16 Grand Challenge and Hubei Cancer Hospital. In our experiments, the average AUC was 0.95 in the test set for pixel-level detection.
Topics: Humans; Image Processing, Computer-Assisted; Neoplasm Metastasis; Pathology
PubMed: 33979398
DOI: 10.1371/journal.pone.0251521 -
The American Journal of Dermatopathology Mar 2018To review the Royal College of Pathologists of Australasia (RCPA) Quality Assurance Program Dermatopathology module from 2005 to 2016 to assess diagnostic performance,... (Review)
Review
AIMS
To review the Royal College of Pathologists of Australasia (RCPA) Quality Assurance Program Dermatopathology module from 2005 to 2016 to assess diagnostic performance, changes over time, and areas of diagnostic difficulty.
METHODS
The computerized records of the RCPA Dermatopathology subspecialist module were reviewed. Cases were categorized into groups including nonneoplastic disorders, neoplasms, and cases with multiple diagnoses. The performance of participants over time in each of these categories and in more specific areas (including melanocytic and adnexal neoplasms) was assessed. Cases which showed high rates of discordant responses were specifically reviewed.
RESULTS
One hundred sixteen cases circulated over 10 years were evaluated. The overall concordance rate was 77%, with a major discordance rate of 7%. There was a slightly higher concordance rate for neoplasms compared with nonneoplastic lesions (80% vs. 74%). Specific areas associated with lower concordance rates included classification of adnexal tumors and identification of multiple pathologies. A spindle cell nevus of Reed yielded a 40% discordance rate, with most misclassifications indicating melanoma.
CONCLUSIONS
The RCPA quality assurance program module has circulated a wide range of common and uncommon cases to participants over the 12 years studied, highlighting a low but important rate of major discordant responses. Melanocytic lesions, hematolymphoid infiltrates, adnexal tumors, and identification of multiple pathologies are identified as areas worthy of particular attention in quality improvement activities.
Topics: Dermatology; Humans; Observer Variation; Pathology; Quality Assurance, Health Care; Retrospective Studies; Skin Diseases
PubMed: 28953010
DOI: 10.1097/DAD.0000000000000922 -
Archives of Pathology & Laboratory... Aug 2012New Frontiers in Pathology is a unique educational event intended to meet the ongoing educational needs of practicing pathologists. Continuous medical education (CME) is...
New Frontiers in Pathology is a unique educational event intended to meet the ongoing educational needs of practicing pathologists. Continuous medical education (CME) is required for maintenance of licensure by virtually all state licensing bodies. Satisfying CME requirements hinges on earning a minimum number of American Medical Association Physician Recognition Award category 1 credits through various activities, including courses like New Frontiers in Pathology that are accredited by the Accreditation Council for Continuing Medical Education. Self-assessment modules (SAMs) are a key component of the American Board of Pathology expectations for maintenance of board certification. Beginning in 2006, the American Board of Pathology granted only time-limited certificates as part of an American Board of Medical Specialties-wide process for maintenance of board certification. Maintenance of board certification has requirements in 4 categories: professional standing, life-long learning and self-assessment, cognitive expertise, and evaluation of performance in practice. Life-long learning and self-assessment includes not only the traditional elements of CME but also the SAMs that are defined as educational products comprising self-administered examinations with a predetermined minimum performance level and a mechanism for receiving feedback. New Frontiers in Pathology will offer SAMs, in addition to the American Medical Association Physician Recognition Award category 1 credits, which it has been accredited to do since its inception, at its 2012 conference scheduled for August 3 through August 5 at The Homestead Resort, Michigan's largest waterfront resort on beautiful Lake Michigan.
Topics: Education, Medical, Continuing; Humans; Michigan; Pathology, Clinical; Self-Assessment; Specialty Boards; United States
PubMed: 22849730
DOI: 10.5858/arpa.2012-0234-ED -
Laboratory Investigation; a Journal of... Nov 2023Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows... (Review)
Review
Digital pathology has transformed the traditional pathology practice of analyzing tissue under a microscope into a computer vision workflow. Whole-slide imaging allows pathologists to view and analyze microscopic images on a computer monitor, enabling computational pathology. By leveraging artificial intelligence (AI) and machine learning (ML), computational pathology has emerged as a promising field in recent years. Recently, task-specific AI/ML (eg, convolutional neural networks) has risen to the forefront, achieving above-human performance in many image-processing and computer vision tasks. The performance of task-specific AI/ML models depends on the availability of many annotated training datasets, which presents a rate-limiting factor for AI/ML development in pathology. Task-specific AI/ML models cannot benefit from multimodal data and lack generalization, eg, the AI models often struggle to generalize to new datasets or unseen variations in image acquisition, staining techniques, or tissue types. The 2020s are witnessing the rise of foundation models and generative AI. A foundation model is a large AI model trained using sizable data, which is later adapted (or fine-tuned) to perform different tasks using a modest amount of task-specific annotated data. These AI models provide in-context learning, can self-correct mistakes, and promptly adjust to user feedback. In this review, we provide a brief overview of recent advances in computational pathology enabled by task-specific AI, their challenges and limitations, and then introduce various foundation models. We propose to create a pathology-specific generative AI based on multimodal foundation models and present its potentially transformative role in digital pathology. We describe different use cases, delineating how it could serve as an expert companion of pathologists and help them efficiently and objectively perform routine laboratory tasks, including quantifying image analysis, generating pathology reports, diagnosis, and prognosis. We also outline the potential role that foundation models and generative AI can play in standardizing the pathology laboratory workflow, education, and training.
Topics: Humans; Artificial Intelligence; Image Processing, Computer-Assisted; Machine Learning; Neural Networks, Computer; Pathologists; Pathology
PubMed: 37757969
DOI: 10.1016/j.labinv.2023.100255 -
Veterinary Pathology Sep 2017Using light microscopy to describe the microarchitecture of normal and diseased tissues has changed very little since the middle of the 19th century. While the premise... (Review)
Review
Using light microscopy to describe the microarchitecture of normal and diseased tissues has changed very little since the middle of the 19th century. While the premise of histologic analysis remains intact, our relationship with the microscope is changing dramatically. Digital pathology offers new forms of visualization, and delivery of images is facilitated in unprecedented ways. This new technology can untether us entirely from our light microscopes, with many pathologists already performing their jobs using virtual microscopy. Several veterinary colleges have integrated virtual microscopy in their curriculum, and some diagnostic histopathology labs are switching to virtual microscopy as their main tool for the assessment of histologic specimens. Considering recent technical advancements of slide scanner and viewing software, digital pathology should now be considered a serious alternative to traditional light microscopy. This review therefore intends to give an overview of the current digital pathology technologies and their potential in all fields of veterinary pathology (ie, research, diagnostic service, and education). A future integration of digital pathology in the veterinary pathologist's workflow seems to be inevitable, and therefore it is proposed that trainees should be taught in digital pathology to keep up with the unavoidable digitization of the profession.
Topics: Animals; Computer Communication Networks; Humans; Image Processing, Computer-Assisted; Microscopy; Pathologists; Pathology, Veterinary; Software; Telepathology; User-Computer Interface; Veterinary Medicine
PubMed: 28578626
DOI: 10.1177/0300985817709888 -
The American Journal of Pathology Oct 2021Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in... (Review)
Review
Significant advances in artificial intelligence (AI), deep learning, and other machine-learning approaches have been made in recent years, with applications found in almost every industry, including health care. AI is capable of completing a spectrum of mundane to complex medically oriented tasks previously performed only by boarded physicians, most recently assisting with the detection of cancers difficult to find on histopathology slides. Although computers will likely not replace pathologists any time soon, properly designed AI-based tools hold great potential for increasing workflow efficiency and diagnostic accuracy in pathology. Recent trends, such as data augmentation, crowdsourcing for generating annotated data sets, and unsupervised learning with molecular and/or clinical outcomes versus human diagnoses as a source of ground truth, are eliminating the direct role of pathologists in algorithm development. Proper integration of AI-based systems into anatomic-pathology practice will necessarily require fully digital imaging platforms, an overhaul of legacy information-technology infrastructures, modification of laboratory/pathologist workflows, appropriate reimbursement/cost-offsetting models, and ultimately, the active participation of pathologists to encourage buy-in and oversight. Regulations tailored to the nature and limitations of AI are currently in development and, when instituted, are expected to promote safe and effective use. This review addresses the challenges in AI development, deployment, and regulation to be overcome prior to its widespread adoption in anatomic pathology.
Topics: Artificial Intelligence; Cloud Computing; Humans; Pathologists; Pathology; Practice Patterns, Physicians'; Social Control, Formal
PubMed: 33245914
DOI: 10.1016/j.ajpath.2020.10.018 -
American Journal of Clinical Pathology Nov 2021Corruption is a widely acknowledged problem in the health sector of low- and middle-income countries (LMICs). Yet, little is known about the types of corruption that... (Review)
Review
OBJECTIVES
Corruption is a widely acknowledged problem in the health sector of low- and middle-income countries (LMICs). Yet, little is known about the types of corruption that affect the delivery of pathology and laboratory medicine (PALM) services. This review is a first step at examining corruption risks in PALM.
METHODS
We performed a critical review of medical literature focused on health sector corruption in LMICs. To provide context, we categorized cases of laboratory-related fraud and abuse in the United States.
RESULTS
Forms of corruption in LMICs that may affect the provision of PALM services include informal payments, absenteeism, theft and diversion, kickbacks, self-referral, and fraudulent billing.
CONCLUSIONS
Corruption represents a functional reality in many LMICs and hinders the delivery of services and distribution of resources to which individuals and entities are legally entitled. Further study is needed to estimate the extent of corruption in PALM and develop appropriate anticorruption strategies.
Topics: Fraud; Humans; Laboratories; Pathology; United States
PubMed: 34219146
DOI: 10.1093/ajcp/aqab046 -
Turk Patoloji Dergisi 2018Molecular pathological analysis has an expanding role in patient diagnosis and management. The performance of these techniques relies on excellent laboratory procedures.... (Review)
Review
Molecular pathological analysis has an expanding role in patient diagnosis and management. The performance of these techniques relies on excellent laboratory procedures. However, the crucial step is obtaining the best samples for molecular analysis. Archiving and selection of these are the responsibilities of all pathologists even if they are not working at a center with molecular pathological facilities. This review focuses on the features of different types of materials for molecular pathological analysis. Many steps that might affect the results, including communication between the pathologist and the oncology team, features of different types of materials (cytological, tissue blocks, biopsy, circulating tumor cells (CTCs) and cell-free circulating nucleic acids), effects of tissue processing, methods for selecting the best material, and tissue saving and tumor enrichment methods are discussed. The procedures for referral to a center for molecular pathological analysis are also mentioned. Awareness of the importance of the cytopathological and histopathological material of the patients for future molecular pathological analysis by pathologists is of the utmost importance.
Topics: Humans; Pathologists; Pathology, Clinical; Pathology, Molecular
PubMed: 29235614
DOI: 10.5146/tjpath.2017.01420 -
Archives of Pathology & Laboratory... Sep 2019The rapid evolution of optical imaging modalities in recent years has opened the opportunity for ex vivo tissue imaging, which has significant implications for surgical... (Review)
Review
CONTEXT.—
The rapid evolution of optical imaging modalities in recent years has opened the opportunity for ex vivo tissue imaging, which has significant implications for surgical pathology practice. These modalities have promising potential to be used as next-generation digital microscopy tools for examination of fresh tissue, with or without labeling with contrast agents.
OBJECTIVE.—
To review the literature regarding various types of ex vivo optical imaging platforms that can generate digital images for tissue recognition with potential for utilization in anatomic pathology clinical practices.
DATA SOURCES.—
Literature relevant to ex vivo tissue imaging obtained from the PubMed database.
CONCLUSIONS.—
Ex vivo imaging of tissues can be performed by using various types of optical imaging techniques. These next-generation digital microscopy tools have a promising potential for utilization in surgical pathology practice.
Topics: Humans; Clinical Laboratory Techniques; Microscopy; Microscopy, Confocal; Microscopy, Fluorescence; Microscopy, Ultraviolet; Nonlinear Optical Microscopy; Optical Imaging; Pathology, Clinical; Pathology, Surgical; Tomography, Optical Coherence
PubMed: 31295016
DOI: 10.5858/arpa.2019-0058-RA -
Advances in Anatomic Pathology May 2016First developed in 1957, confocal microscopy is a powerful imaging tool that can be used to obtain near real-time reflected light images of untreated human tissue with... (Review)
Review
First developed in 1957, confocal microscopy is a powerful imaging tool that can be used to obtain near real-time reflected light images of untreated human tissue with nearly histologic resolution. Besides its research applications, in the last decades, confocal microscopy technology has been proposed as a useful device to improve clinical diagnosis, especially in ophthalmology, dermatology, and endomicroscopy settings, thanks to advances in instrument development. Compared with the wider use of the in vivo tissue assessment, ex vivo applications of confocal microscopy are not fully explored. A comprehensive review of the current literature was performed here, focusing on the reliable applications of ex vivo confocal microscopy in surgical pathology and on some potential evolutions of this new technique from pathologists' viewpoint.
Topics: Fluorescence; Microscopy, Confocal; Pathology, Surgical
PubMed: 27058244
DOI: 10.1097/PAP.0000000000000114