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Viet CT, Zhang M, Dharmaraj N, Li GY, Pearson AT, Manon VA, Grandhi A, Xu K, Aouizerat BE, Young S. Artificial Intelligence Applications in Oral Cancer and Oral Dysplasia. Tissue Eng Part A 2024. [PMID: 39041628 DOI: 10.1089/ten.tea.2024.0096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/24/2024] Open
Abstract
Oral squamous cell carcinoma (OSCC) is a highly unpredictable disease with devastating mortality rates that have not changed over the past decades, in the face of advancements in treatments and biomarkers, which have improved survival for other cancers. Delays in diagnosis are frequent, leading to more disfiguring treatments and poor outcomes in patients. The clinical challenge lies in identifying those patients at highest risk for developing OSCC. Oral epithelial dysplasia (OED) is a precursor of OSCC with highly variable behavior across patients. There is no reliable clinical, pathologic, histologic or molecular biomarker to determine individual risk in OED patients. Similarly, there are no robust biomarkers to predict treatment outcomes or mortality of OSCC patients. This review aims to highlight advancements in artificial intelligence (AI)-based methods to develop predictive biomarkers of OED transformation to OSCC or predictive biomarkers of OSCC mortality and treatment response. Machine-learning based biomarkers, such as S100A7, demonstrate promising appraisal for the risk of malignant transformation of OED. Machine learning-enhanced multiplex immunohistochemistry (mIHC) workflows examine immune cell patterns and organization within the tumor immune microenvironment to generate outcome predictions in immunotherapy. Deep learning (DL) is an AI-based method using an extended neural network or related architecture with multiple "hidden" layers of simulated neurons to combine simple visual features into complex patterns. DL-based digital pathology is currently being developed to assess OED and OSCC outcomes. The integration of machine learning in epigenomics aims to examine the epigenetic modification of diseases and improve our ability to detect, classify, and predict outcomes associated with epigenetic marks. Collectively, these tools showcase promising advancements in discovery and technology, which may provide a potential solution to addressing the current limitations in predicting OED transformation and OSCC behavior, both of which are clinical challenges that must be addressed in order to improve OSCC survival.
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Affiliation(s)
- Chi Tonglien Viet
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Michael Zhang
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Neeraja Dharmaraj
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Grace Y Li
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Alexander T Pearson
- The University of Chicago Medical Center, Department of Medicine, Section of Hematology/Oncology,, Chicago, Illinois, United States;
| | - Victoria A Manon
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
| | - Anupama Grandhi
- Loma Linda University, Department of Oral and Maxillofacial Surgery, Loma Linda, California, United States;
| | - Ke Xu
- Yale School of Medicine, Department of Psychiatry, New Haven, Connecticut, United States
- VA Connecticut Healthcare System - West Haven Campus, West Haven, Connecticut, United States;
| | - Bradley E Aouizerat
- New York University College of Dentistry, Translational Research Center, New York, New York, United States;
| | - Simon Young
- The University of Texas Health Science Center at Houston School of Dentistry, Bernard & Gloria Pepper Katz Department of Oral and Maxillofacial Surgery, Houston, Texas, United States;
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Wei Y, Deng Y, Sun C, Lin M, Jiang H, Peng Y. Deep learning with noisy labels in medical prediction problems: a scoping review. J Am Med Inform Assoc 2024; 31:1596-1607. [PMID: 38814164 PMCID: PMC11187424 DOI: 10.1093/jamia/ocae108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2024] [Revised: 04/27/2024] [Accepted: 05/03/2024] [Indexed: 05/31/2024] Open
Abstract
OBJECTIVES Medical research faces substantial challenges from noisy labels attributed to factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of label noise management remains limited, and label noise is largely ignored. To this end, there is a critical need to conduct a scoping review focusing on the problem space. This scoping review aims to comprehensively review label noise management in deep learning-based medical prediction problems, which includes label noise detection, label noise handling, and evaluation. Research involving label uncertainty is also included. METHODS Our scoping review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. We searched 4 databases, including PubMed, IEEE Xplore, Google Scholar, and Semantic Scholar. Our search terms include "noisy label AND medical/healthcare/clinical," "uncertainty AND medical/healthcare/clinical," and "noise AND medical/healthcare/clinical." RESULTS A total of 60 papers met inclusion criteria between 2016 and 2023. A series of practical questions in medical research are investigated. These include the sources of label noise, the impact of label noise, the detection of label noise, label noise handling techniques, and their evaluation. Categorization of both label noise detection methods and handling techniques are provided. DISCUSSION From a methodological perspective, we observe that the medical community has been up to date with the broader deep-learning community, given that most techniques have been evaluated on medical data. We recommend considering label noise as a standard element in medical research, even if it is not dedicated to handling noisy labels. Initial experiments can start with easy-to-implement methods, such as noise-robust loss functions, weighting, and curriculum learning.
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Affiliation(s)
- Yishu Wei
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Reddit Inc., San Francisco, CA 16093, United States
| | - Yu Deng
- Center for Health Information Partnerships, Northwestern University, Chicago, IL 10611, United States
| | - Cong Sun
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
| | - Mingquan Lin
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
- Department of Surgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Hongmei Jiang
- Department of Statistics and Data Science, Northwestern University, Evanston, IL 60208, United States
| | - Yifan Peng
- Department of Population Health Sciences, Weill Cornell Medicine, New York, NY 10065, United States
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Huang L, Ruan S, Xing Y, Feng M. A review of uncertainty quantification in medical image analysis: Probabilistic and non-probabilistic methods. Med Image Anal 2024; 97:103223. [PMID: 38861770 DOI: 10.1016/j.media.2024.103223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2023] [Revised: 03/16/2024] [Accepted: 05/27/2024] [Indexed: 06/13/2024]
Abstract
The comprehensive integration of machine learning healthcare models within clinical practice remains suboptimal, notwithstanding the proliferation of high-performing solutions reported in the literature. A predominant factor hindering widespread adoption pertains to an insufficiency of evidence affirming the reliability of the aforementioned models. Recently, uncertainty quantification methods have been proposed as a potential solution to quantify the reliability of machine learning models and thus increase the interpretability and acceptability of the results. In this review, we offer a comprehensive overview of the prevailing methods proposed to quantify the uncertainty inherent in machine learning models developed for various medical image tasks. Contrary to earlier reviews that exclusively focused on probabilistic methods, this review also explores non-probabilistic approaches, thereby furnishing a more holistic survey of research pertaining to uncertainty quantification for machine learning models. Analysis of medical images with the summary and discussion on medical applications and the corresponding uncertainty evaluation protocols are presented, which focus on the specific challenges of uncertainty in medical image analysis. We also highlight some potential future research work at the end. Generally, this review aims to allow researchers from both clinical and technical backgrounds to gain a quick and yet in-depth understanding of the research in uncertainty quantification for medical image analysis machine learning models.
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Affiliation(s)
- Ling Huang
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Su Ruan
- Quantif, LITIS, University of Rouen Normandy, France.
| | - Yucheng Xing
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Mengling Feng
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Institute of Data Science, National University of Singapore, Singapore
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Kanwal N, López-Pérez M, Kiraz U, Zuiverloon TCM, Molina R, Engan K. Are you sure it's an artifact? Artifact detection and uncertainty quantification in histological images. Comput Med Imaging Graph 2024; 112:102321. [PMID: 38199127 DOI: 10.1016/j.compmedimag.2023.102321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2023] [Revised: 11/08/2023] [Accepted: 12/12/2023] [Indexed: 01/12/2024]
Abstract
Modern cancer diagnostics involves extracting tissue specimens from suspicious areas and conducting histotechnical procedures to prepare a digitized glass slide, called Whole Slide Image (WSI), for further examination. These procedures frequently introduce different types of artifacts in the obtained WSI, and histological artifacts might influence Computational Pathology (CPATH) systems further down to a diagnostic pipeline if not excluded or handled. Deep Convolutional Neural Networks (DCNNs) have achieved promising results for the detection of some WSI artifacts, however, they do not incorporate uncertainty in their predictions. This paper proposes an uncertainty-aware Deep Kernel Learning (DKL) model to detect blurry areas and folded tissues, two types of artifacts that can appear in WSIs. The proposed probabilistic model combines a CNN feature extractor and a sparse Gaussian Processes (GPs) classifier, which improves the performance of current state-of-the-art artifact detection DCNNs and provides uncertainty estimates. We achieved 0.996 and 0.938 F1 scores for blur and folded tissue detection on unseen data, respectively. In extensive experiments, we validated the DKL model on unseen data from external independent cohorts with different staining and tissue types, where it outperformed DCNNs. Interestingly, the DKL model is more confident in the correct predictions and less in the wrong ones. The proposed DKL model can be integrated into the preprocessing pipeline of CPATH systems to provide reliable predictions and possibly serve as a quality control tool.
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Affiliation(s)
- Neel Kanwal
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Stavanger, Norway.
| | - Miguel López-Pérez
- Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
| | - Umay Kiraz
- Department of Pathology, Stavanger University Hospital, 4011 Stavanger, Norway; Department of Chemistry, Bioscience and Environmental Engineering, University of Stavanger, 4021 Stavanger, Norway
| | - Tahlita C M Zuiverloon
- Department of Urology, University Medical Center Rotterdam, Erasmus MC Cancer Institute, 1035 GD Rotterdam, The Netherlands
| | - Rafael Molina
- Department of Computer Science and Artificial Intelligence, University of Granada, 18071 Granada, Spain
| | - Kjersti Engan
- Department of Electrical Engineering and Computer Science, University of Stavanger, 4021 Stavanger, Norway
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Del Amor R, Pérez-Cano J, López-Pérez M, Terradez L, Aneiros-Fernandez J, Morales S, Mateos J, Molina R, Naranjo V. Annotation protocol and crowdsourcing multiple instance learning classification of skin histological images: The CR-AI4SkIN dataset. Artif Intell Med 2023; 145:102686. [PMID: 37925214 DOI: 10.1016/j.artmed.2023.102686] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Revised: 10/11/2023] [Accepted: 10/13/2023] [Indexed: 11/06/2023]
Abstract
Digital Pathology (DP) has experienced a significant growth in recent years and has become an essential tool for diagnosing and prognosis of tumors. The availability of Whole Slide Images (WSIs) and the implementation of Deep Learning (DL) algorithms have paved the way for the appearance of Artificial Intelligence (AI) systems that support the diagnosis process. These systems require extensive and varied data for their training to be successful. However, creating labeled datasets in histopathology is laborious and time-consuming. We have developed a crowdsourcing-multiple instance labeling/learning protocol that is applied to the creation and use of the CR-AI4SkIN dataset.2 CR-AI4SkIN contains 271 WSIs of 7 Cutaneous Spindle Cell (CSC) neoplasms with expert and non-expert labels at region and WSI levels. It is the first dataset of these types of neoplasms made available. The regions selected by the experts are used to learn an automatic extractor of Regions of Interest (ROIs) from WSIs. To produce the embedding of each WSI, the representations of patches within the ROIs are obtained using a contrastive learning method, and then combined. Finally, they are fed to a Gaussian process-based crowdsourcing classifier, which utilizes the noisy non-expert WSI labels. We validate our crowdsourcing-multiple instance learning method in the CR-AI4SkIN dataset, addressing a binary classification problem (malign vs. benign). The proposed method obtains an F1 score of 0.7911 on the test set, outperforming three widely used aggregation methods for crowdsourcing tasks. Furthermore, our crowdsourcing method also outperforms the supervised model with expert labels on the test set (F1-score = 0.6035). The promising results support the proposed crowdsourcing multiple instance learning annotation protocol. It also validates the automatic extraction of interest regions and the use of contrastive embedding and Gaussian process classification to perform crowdsourcing classification tasks.
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Affiliation(s)
- Rocío Del Amor
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Jose Pérez-Cano
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Miguel López-Pérez
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain.
| | - Liria Terradez
- Pathology Department. Hospital Clínico Universitario de Valencia, Universidad de Valencia, Spain
| | | | - Sandra Morales
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
| | - Javier Mateos
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Rafael Molina
- Department of Computer Science and Artificial Intelligence, University of Granada, 18010 Granada, Spain
| | - Valery Naranjo
- Instituto Universitario de Investigación en Tecnología Centrada en el Ser Humano, Universitat Politècnica de València, Valencia, Spain
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