Our vision is to prototype tailor-made computational medical image analysis solutions for digital pathology. Our scope includes image analysis and general bio-image quantification methods of pathology images such as from Hematoxylin-Eosin (H&E) and Immunohistochemistry (IHC) staining. Our team focuses on the following topics in digital pathology and biomedical image analysis:
With recent advances in digital image technology and artificial intelligence, histology laboratories and standards are undergoing fundamental change. Automated quality assessment tools are essential for the integration of modern digital solutions into day-to-day clinical practice and the evaluation of image quality remains an ongoing area of research. In order to calculate an image's non-reference image quality score, functional estimators of perceptual image quality are necessary. Traditional estimators of image quality typically prove to have higher efficiency on natural images compared with histological images. While several tools have been previously developed for histological images, estimators of quality assessment in H&E images remain limited to focal accuracy. To address this issue, we have developed a dedicated solution specifically to enhance nuclear image quality assessment. At present, our software package has been validated by industry-renown digital image scanner manufacturers.
We have worked closely with a number of pathologists to develop a deep learning based model for automated Gleason Grading in prostate histopathological images which learns from professionally curated images. Based on expert annotations, the AI model is trained to differentiate Gleason Pattern 3, Gleason Pattern 4, Gleason Pattern 5, normal glands, stroma and other structures. At present, the AI has proven able to detect malignant regions at different magnifications and alert pathologists to certain, small malignant regions. This automatic detection increases the accuracy, efficiency and consistency of the Gleason Grading and is designed to alleviate the manual burden of quantitative tumour assessment. It is also applicable to images scanned by scanners from different brands such as Akoya Biosciences, Olympus, Zeiss, KFBio and Leica. Moreover, the preliminary model we’ve built is capable of incremental learning, which means the model’s performance will improve gradually with the addition of new training data collected through its usage. With every interaction between the AI model and pathologists, new annotated data can be collected more efficiently to enhance the model and to benefit scientific research.
Mitotic count is a crucial clinical marker in breast cancer diagnosis. We’ve developed a pipeline for semi-auto mitotic figure detection in breast cancer histopathological images which were validated on three different data sets: SGH dataset, ICPR2012 and ICPR2014. Since Hematoxylin stains only the nuclei, where mitosis occurs, Haematoxylin and eosin channels are first separated by colour deconvolution and then analysed based on the intensity features of mitotic figures in the Hematoxylin channel. The three most discriminative features were selected to train a Naïve Bayes model to differentiate mitotic figures and background. By applying the trained model, regions that don’t possess mitotic figures can be filtered and only those that do are retained (~15% of the whole area) for the user’s evaluation. The output of the model assists the pathologists in locating ‘mitosis hot spots’ when counting the number of mitotic figures. This feature is applicable to images, including TMA images, of various size.
Cervical cancer can be effectively treated if diagnosed early. Artificial intelligence has the potential to reduce pathologists’ workload, improve diagnostic speed and maintain high accuracy for cervical cytology screening. To improve diagnostic accuracy and speed, we developed an AI assistive diagnosis system to classify cervical liquid-based thin-layer cell smears in accordance with TBS criteria.In this study, we trained our models with >81,000 samples retrospectively. We used YOLOv3 to develop the target detection model (mAP, Mean Average Precision, up to 0.8233), Xception algorithm and Patch-based classification to boost the detection target classification model (accuracy rates of 0.968 and 0.9192, respectively), U-net to segment the nucleus and accurately measure the gray value of the nucleus ( Mean intersection, mIOU reaches 0.8356. We integrated XGBoost and logical trees together with the above deep learning models based on optimized features given by the deep learning process, formulated a complete cervical liquid-based cytology smear TBS classification and diagnosis pipeline, and successfully established an artificial intelligence-assisted diagnosis system. The optimized system was validated with >34,000 prospectively samples collected from 11 medical Institutions.
Yu Weimiao is an image processing and AI/ML expert in bioimage informatics and computational digital pathology. He is currently a joint Principal Investigator and group leader of IMCB and BII, leading two groups to address different challenging problems in the field. Dr. Yu obtained his Ph.D. from the National University of Singapore (NUS) in 2007. He joined Agency of Science, Technology, and Research(A*STAR) in 2007. His research interests are Computational Biomedical Image Analysis and Quantitative Imaging Informatics based on AI and machine learning. His research outcomes were published in top international peer-reviewed journals, such as Nature Cell Biology, Nature Communication, Breast Cancer Research, Bioinformatics, Current Biology, etc. Dr. Yu and his teams focus on pushing the quantitative and reliable cellular/ molecular image analysis and diagnosis solutions from academic research to clinical decision making. The previous research and R&D projects with hospitals, biotech, and pharmaceutical companies equipped his teams with solid skills and expertise in medical algorithms development and validation. His teams integrate methodologies from multiple fields, such as signal processing, image processing and computer vision, optimization, machine learning, pattern recognition, mathematical modelling, Artificial Intelligence (AI), and Deep Learning (DL) with the input of pathologists, oncologists, and cell biologists. Dr. Yu is not only active in academic research; he is also an entrepreneur. He co-founded a biotech company, known as A!maginostic Pte. Ltd. Dr. Yu has established close collaboration with the Pathology Division/Department of three clinical research centers, Singapore General Hospital (SGH), National University Hospital (NUH) and Tan Tock Seng Hospital (TTSH), to ensure a smooth R&D pipeline and data source of developing the computational digital pathology solutions. Dr. Yu established a world-class joint lab of excellence for immunodiagnosis at the tissue level. Such a platform allows the researchers, clinicians, and pharma to profile the patient immune signature for diagnosis, prognosis and drug response study, etc.
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