Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

IntroductionDeep learning techniques are gaining momentum in medical research. Evidence shows that deep learning has advantages over humans in image identification and classification, such as facial image analysis in detecting people’s medical conditions. While positive findings are available, little is known about the state-of-the-art of deep learning-based facial image analysis in the medical context. For the consideration of patients’ welfare and the development of the practice, a timely understanding of the challenges and opportunities faced by research on deep-learning-based facial image analysis is needed. To address this gap, we aim to conduct a systematic review to identify the characteristics and effects of deep learning-based facial image analysis in medical research. Insights gained from this systematic review will provide a much-needed understanding of the characteristics, challenges, as well as opportunities in deep learning-based facial image analysis applied in the contexts of disease detection, diagnosis and prognosis.MethodsDatabases including PubMed, PsycINFO, CINAHL, IEEEXplore and Scopus will be searched for relevant studies published in English in September, 2021. Titles, abstracts and full-text articles will be screened to identify eligible articles. A manual search of the reference lists of the included articles will also be conducted. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was adopted to guide the systematic review process. Two reviewers will independently examine the citations and select studies for inclusion. Discrepancies will be resolved by group discussions till a consensus is reached. Data will be extracted based on the research objective and selection criteria adopted in this study.Ethics and disseminationAs the study is a protocol for a systematic review, ethical approval is not required. The study findings will be disseminated via peer-reviewed publications and conference presentations.PROSPERO registration numberCRD42020196473.

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Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review

Cancers ◽

10.3390/cancers12071884 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1884 ◽

Cited By ~ 2

Author(s):

Nishant Thakur ◽

Hongjun Yoon ◽

Yosep Chong

Keyword(s):

Artificial Intelligence ◽

Colorectal Cancer ◽

Systematic Review ◽

Image Analysis ◽

State Of The Art ◽

The State ◽

Cochrane Library ◽

Routine Practice ◽

Cancer Pathology ◽

Pathology Image

Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

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Artificial Intelligence in Lung Cancer Pathology Image Analysis

Cancers ◽

10.3390/cancers11111673 ◽

2019 ◽

Vol 11 (11) ◽

pp. 1673 ◽

Cited By ~ 16

Author(s):

Shidan Wang ◽

Donghan M. Yang ◽

Ruichen Rong ◽

Xiaowei Zhan ◽

Junya Fujimoto ◽

...

Keyword(s):

Artificial Intelligence ◽

Lung Cancer ◽

Image Analysis ◽

Deep Learning ◽

Digital Pathology ◽

Diagnosis And Prognosis ◽

Model Interpretation ◽

Lung Cancer Diagnosis ◽

Cancer Pathology ◽

Pathology Image

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

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Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review

Postgraduate Medical Journal ◽

10.1136/postgradmedj-2020-139620 ◽

2021 ◽

pp. postgradmedj-2020-139620

Author(s):

Natesh Shivakumar ◽

Anirudh Chandrashekar ◽

Ashok Inderraj Handa ◽

Regent Lee

Keyword(s):

Systematic Review ◽

Image Analysis ◽

Deep Learning ◽

Metastatic Disease ◽

Potential Role ◽

Primary Tumour ◽

Ct Image ◽

Computerised Tomography ◽

Clinical Value ◽

Ct Image Analysis

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis. Deep learning (DL), a form of machine learning, where layers of programmed algorithms interpret and recognise patterns, may have a potential role in CT image analysis. This review aims to provide an overview on the use of DL in CT image analysis in the diagnostic evaluation of metastatic disease. A total of 29 studies were included which could be grouped together into three areas of research: the use of deep learning on the detection of metastatic disease from CT imaging, characterisation of lesions on CT into metastasis and prediction of the presence or development of metastasis based on the primary tumour. In conclusion, DL in CT image analysis could have a potential role in evaluating metastatic disease; however, prospective clinical trials investigating its clinical value are required.

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pymia: A Python package for data handling and evaluation in deep learning-based medical image analysis

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2020.105796 ◽

2021 ◽

Vol 198 ◽

pp. 105796

Author(s):

Alain Jungo ◽

Olivier Scheidegger ◽

Mauricio Reyes ◽

Fabian Balsiger

Keyword(s):

Image Analysis ◽

Deep Learning ◽

Medical Image ◽

Medical Image Analysis ◽

Data Handling ◽

Python Package

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KloudNet: Deep Learning for Sky Image Analysis and Irradiance Forecasting

Lecture Notes in Computer Science - Pattern Recognition ◽

10.1007/978-3-030-12939-2_37 ◽

2019 ◽

pp. 535-551 ◽

Cited By ~ 3

Author(s):

Dinesh Pothineni ◽

Martin R. Oswald ◽

Jan Poland ◽

Marc Pollefeys

Keyword(s):

Image Analysis ◽

Deep Learning

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Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

Plant Methods ◽

10.1186/s13007-021-00747-0 ◽

2021 ◽

Vol 17 (1) ◽

Author(s):

Shuo Zhou ◽

Xiujuan Chai ◽

Zixuan Yang ◽

Hongwu Wang ◽

Chenxue Yang ◽

...

Keyword(s):

Computer Vision ◽

Image Analysis ◽

Deep Learning ◽

High Throughput ◽

Batch Processing ◽

Plant Phenotyping ◽

Plant Science ◽

Analysis Software ◽

Image Analysis Software ◽

Maize Growth

Abstract Background Maize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets. Results On the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625. Conclusion The Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

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