Seminars 23/24
The TIA seminar is a regular event, organised by the Tissue Image Analytics Centre, that usually takes place on the 1st and 3rd Mondays of each month between 2pm and 3pm. We invite researchers and leaders in the area of computational pathology and associated areas to discuss their work and stimulate thought-provoking discussions.
Take a look at the details of TIA seminars during 2022-23 academic year, most of which are also available on our YouTube channel.
Please contact the TIA Seminar Organiser Dr Adam Shephard via email (adam.shephard@warwick.ac.uk) if you are interested in attending any of the seminars below.
Spring 2023
| Date | Speaker | Title |
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15th January 2024 |
Gabriele Campanella Icahn School of Medicine at Mount Sinai |
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5th February 2024 |
Georg Wölflein University of St Andrews |
A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology |
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19th February 2024 |
Gregory Verghese King’s College London |
Autumn 2023
| Date | Speaker | Title |
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2nd October 2023 |
Zhi Huang Stanford University |
A visual–language foundation model for pathology image analysis using medical Twitter
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16th October 2023 |
Yuri Tolkach University Hospital Cologne |
Using artificial intelligence for diagnostic and prognostic algorithms in gastrointestinal cancers
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6th November 2023 |
Anglin Dent University of Toronto |
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27th November 2023 |
Pingjun Chen University of Texas |
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4th December 2023 |
Jan Brosens University of Warwick |
Endometrial diagnostics for the prediction and prevention of miscarriage |
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11th December 2023 |
Andrew Song Harvard Medical School |
AI-driven efficient patient prognosis based on 3D pathology samples |
Autumn 2023
Dr Zhi Huang
Stanford University, Stanford, USA (click here for a short bio)
2 Oct 2023
Title: A visual–language foundation model for pathology image analysis using medical Twitter
Abstract: The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. We demonstrate the value of this resource by developing pathology language–image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 scores of 0.565–0.832 compared to F1 scores of 0.030–0.481 for previous contrastive language–image pretrained model. Training a simple supervised classifier on top of PLIP embeddings also achieves 2.5% improvement in F1 scores compared to using other supervised model embeddings. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing and education.
Watch the seminar by clicking here.
Dr Yuri Tolkach
University Hospital Cologne, Cologne, Germany (click here for a short bio)
16 Oct 2023
Title: Using artificial intelligence for diagnostic and prognostic algorithms in gastrointestinal cancers
Abstract: In my talk I will cover how deep learning can be used for creation of diagnostic and prognostic algorithms for two types of GI cancers: oesophageal cancer and colorectal cancer. I will present information from three of our recent studies that show that AI-based algorithms can be seen as powerful assisting tools that allow for more precise diagnosis, creation of new and revision of old morphology-based prognostic parameters, and for saving pathologists' time on routine task automatization.
Watch the seminar by clicking here.
Anglin Dent
University of Toronto, Toronto, Canada (click here for a short bio)
6 Nov 2023
Title: HAVOC: Small-scale histomic mapping of cancer biodiversity across large tissue distances using deep neural networks
Abstract: Intratumoral heterogeneity can wreak havoc on current precision medicine strategies because of challenges in sufficient sampling of geographically separated areas of biodiversity distributed across centimeter-scale tumor distances. To address this gap, our group has developed a deep learning pipeline that leverages histomorphologic fingerprints of tissue to create “Histomic Atlases of Variation Of Cancers” (HAVOC). In this presentation, I will discuss how HAVOC can define regional cancer boundaries with distinct biology, map biodiversity across multiple tissue sections and tumor types, and reveal key biological insights of regionally distinct tumor populations distributed within these tumors.
Watch the seminar by clicking here.
Dr Pingjun Chen
University of Texas, Houston, USA (click here for a short bio)
27 Nov 2023
Title: Pathomic features reveal immune and molecular evolution from lung preneoplasia to invasive adenocarcinoma
Abstract: Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, primarily due to insufficient materials from ADC precursors. In this talk, I will present our study employing state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used H&E histopathology images and extract nine biology-relevant pathomic features to decode lung preneoplasia evolution. Extracted pathomic features revealed a progressive increase of atypical epithelial cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine H&E staining.
Watch the seminar by clicking here.
Prof Jan Brosens
University of Warwick, Coventry, UK (click here for a short bio)
4 Dec 2023 (10:30am)
Title: Endometrial diagnostics for the prediction and prevention of miscarriage
Abstract: Compared to most mammals, human pregnancy is unusual in that it involves chromosomally diverse embryos, cyclical shedding and regeneration of the uterine mucosa (endometrium), and intimate integration of fetal and maternal cells at the uteroplacental interface. Not surprisingly, pregnancy often falters in early gestation. Whether these losses result in clinical miscarriages depends on the origins and impacts of chromosomal errors on fetal development and the ability of the cycling endometrium to transition successfully into the decidual of pregnancy, which anchors and supports the placenta throughout gestation. Aneuploidy originating in oocytes during meiosis drives the age-related risk of miscarriage. By contrast, the frequency of endometrial cycles with an impaired decidual response could theoretically account for the stepwise increase in miscarriage rates with each pregnancy loss independently of maternal age. Based on modelling human embryo implantation in 3D endometrial asssembloids and single cell transcriptomics, we discovered that functionally distinct endometrial stromal cell states render the endometrium receptive to embryo implantation and control the transition into the decidua of pregnancy, respectively. Analysis of over 1,500 endometrial biopsies from non-pregnant women revealed that an imbalance in cell state-specific biomarkers, normalised for the day of the biopsy in the cycle, correlates with the age-independent risk of miscarriage, has a high recurrence rate in different cycles, and is associated with increased risk of a future miscarriage. Further, we demonstrate that the pathological endometrial state associated with miscarriage is caused by loss of tissue plasticity. Current investigations are focussed on determining if machine learning methods on histology whole slide images enhance, complement or improve the performance of molecular methods in predicting pregnancy loss prior to conception.
Dr Andrew Song
Harvard Medical School, Boston, USA (click here for a short bio)
11 Dec 2023
Title: AI-driven efficient patient prognosis based on 3D pathology samples
Abstract: Human tissue forms a microenvironment that is fundamentally three-dimensional (3D). However, the standard-of-care in pathologic diagnosis involves selecting a few two-dimensional (2D) sections for microscopic evaluation, risking sampling bias and misdiagnosis. Diverse methods for capturing 3D tissue morphologies have been developed, with minimal success in translation to clinical practice; manual and computational evaluations of such large 3D data have been impractical and/or unable to provide patient-level clinical insights. Here we present Modality-Agnostic Multiple instance learning for volumetric Block Analysis (MAMBA), a deep-learning-based platform for processing 3D tissue images and predicting patient outcomes. Archived prostate cancer specimens were imaged with open-top light-sheet microscopy or microcomputed tomography and the resulting 3D datasets were used to train risk-stratification networks based on 5-year biochemical recurrence outcomes via MAMBA. With the 3D block-based approach, MAMBA achieves superior prediction performance to 2D traditional single-slice-based prognostication, suggesting superior prognostication with 3D morphological features. Further analyses reveal that the incorporation of greater tissue volume improves prognostic performance and mitigates risk prediction variability from sampling bias, suggesting the value of capturing larger extents of heterogeneous 3D morphology. With the rapid growth and adoption of 3D spatial biology and pathology, MAMBA provides a general and efficient framework for 3D pathology for clinical decision support and can help to reveal novel 3D morphological biomarkers for prognosis and therapeutic response.
Spring 2024
Dr Gabriele Campanella
Icahn School of Medicine at Mount Sinai, New York, USA
15 Jan 2024
Title: Computational Pathology at Health System Scale – Self-Supervised Foundation Models from Three Billion Images
Abstract: TBC
Georg Wölflein
University of St Andrews, St Andrews, UK (click here for a short bio)
5 Feb 2024
Title: A Good Feature Extractor Is All You Need for Weakly Supervised Learning in Histopathology
Abstract: Deep learning is revolutionising pathology, offering novel opportunities in disease prognosis and personalised treatment. Historically, stain normalisation has been a crucial preprocessing step in computational pathology pipelines, and persists into the deep learning era. Yet, with the emergence of feature extractors trained using self-supervised learning (SSL) on diverse pathology datasets, we call this practice into question. In an empirical evaluation of publicly available feature extractors, we find that omitting stain normalisation and image augmentations does not compromise downstream performance, while incurring substantial savings in memory and compute. Further, we show that the top-performing feature extractors are remarkably robust to variations in stain and augmentations like rotation in their latent space. Contrary to previous patch-level benchmarking studies, our approach emphasises clinical relevance by focusing on slide-level prediction tasks in a weakly supervised setting with external validation cohorts. This work represents the most comprehensive robustness evaluation of public pathology SSL feature extractors to date, involving more than 6,000 training runs across nine tasks, five datasets, three downstream architectures, and various preprocessing setups. Our findings stand to streamline digital pathology workflows by minimising preprocessing needs and informing the selection of feature extractors.
Gregory Verghese
King’s College London, London, UK
19 Feb 2024
Title: TBC
Abstract: TBC