Seminars 22/23
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 2021-22 academic year, some 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.
A special thanks to Dr Simon Graham for setting up and organising the 21/22 series (see here)
The 2022/23 TIA Centre Seminar Series has now finished. To see information regarding the 2023/24 series, visit this page.
Summer 2023
| Date | Speaker | Title |
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15th May 2023 |
Azra Raza Columbia University Medical Center |
THE FIRST CELL and the human costs of pursuing cancer to the last
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19th June 2023 |
Germán Corredor Emory University |
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17th July 2023 |
John Le Quesne University of Glasgow |
Self-supervised learning in lung cancer histopathology
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19th July 2023 |
Mahdi Hosseini Concordia University |
Computational Pathology: The Current Trend and Future Challenges
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24th July 2023 |
Andrey Bychkov Kameda Medical Center |
Digital and Computational Pathology in Asia: A Navigation Guide |
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21st August 2023 |
Jason Swedlow University of Dundee |
Making BioImage Data FAIR on a Global Scale: OME’s Bio-Formats, OME-TIFF, OMERO, & IDR
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Spring 2023
| Date | Speaker | Title |
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16th January 2023 |
André Homeyer Fraunhofer MEVIS |
Artificial Intelligence in Pathology: From Prototype to Product
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6th February 2023 |
Sarah Fremond Leiden University Medical Center |
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20th February 2023 |
Oliver Lester Saldanha Uniklinik RWTH Aachen |
Swarm learning for decentralized deep learning in cancer histopathology
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6th March 2023 |
Marc Aubreville Technische Hochschule Ingolstadt |
Mitosis Detection in Arbitrary Tumor Tissue - is the problem solved?
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20th March 2023 |
Hanya Mahmood University of Sheffield |
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18th April 2023 |
Paul Tourniaire Université Côte d'Azur |
MS-CLAM - A Proof-of-Concept for mixed supervision in digital pathology
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Autumn 2022
| Date | Speaker | Title |
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7th November 2022 |
Richard Chen Harvard Medical School |
Context-Aware and Multimodal Computational Pathology
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21st November 2022 |
Mohamed Amgad Tageldin Northwestern University |
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6th December 2022 |
James Diao Harvard Medical School |
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19th December 2022 |
Korsuk Sirinukunwattana University of Oxford |
Image-based consensus molecular subtypes of colorectal cancer
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Autumn 2022
Richard Chen
Harvard Medical School, Boston, USA (click here for a short bio)
7 Nov 2022
Title: Context-Aware and Multimodal Computational Pathology
Abstract: Survival outcome prediction is a challenging weakly-supervised and ordinal regression task in computational pathology that involves modeling complex interactions within the tumor microenvironment in gigapixel whole slide images (WSIs). Despite recent progress in formulating WSIs as bags for multiple instance learning (MIL), representation learning of entire WSIs remains an open and challenging problem, especially in overcoming: 1) the computational complexity of feature aggregation in large bags, and 2) the data heterogeneity gap in incorporating biological priors such as genomic measurements. In this talk, we present recent advances made in slide-level cancer prognostication using Transformer attention for early-based multimodal fusion, context modeling, interpretability. By equipping MIL frameworks with Transformer attention in weakly-supervised learning, we demonstrate improvements in AUC performance and localization of important morphological visual concepts, which may further aid the discovery of novel biomarkers.
Watch the seminar by clicking here.
Dr Mohamed Amgad Tageldin
Northwestern University, Chicago, USA (click here for a short bio)
21 Nov 2022
Title: Utilizing crowdsourcing approaches to curate large-scale datasets for computational pathology applications
Abstract: Deep learning enables accurate high-resolution mapping of cells and tissue structures that can serve as the foundation of interpretable machine-learning models for computational pathology. However, generating adequate labels for these structures is a critical barrier, given the time and effort required from pathologists. We describe a novel collaborative framework for engaging crowds of medical students and pathologists to produce quality labels for cell nuclei. We used this approach to produce the NuCLS dataset, containing >220,000 annotations of cell nuclei in breast cancers. This builds on prior work labeling tissue regions to produce the BCSS dataset to enable the multi-scale identification of tissue regions and nuclei within the breast cancer microenvironment. We present analysis results for single and multi-rater annotations from both non-experts and pathologists. We also share a novel workflow that uses algorithmic suggestions to collect accurate segmentation data without the need for laborious manual tracing of nuclei. Our results indicate that even noisy algorithmic suggestions do not adversely affect pathologist accuracy and can help non-experts improve annotation quality. Finally, we present a new approach for inferring truth from multiple raters and show that non-experts can produce accurate annotations for visually distinctive classes.
Watch the seminar by clicking here.
James A Diao
Harvard Medical School, Boston, USA (click here for a short bio)
6 Dec 2022
Title: Human-interpretable image features derived from densely mapped cancer pathology slides predict diverse molecular phenotype
Abstract: Computational methods have made substantial progress in improving the accuracy and throughput of pathology workflows for diagnostic, prognostic, and genomic prediction. Still, lack of interpretability remains a significant barrier to clinical integration. We present an approach for predicting clinically-relevant molecular phenotypes from whole-slide histopathology images using human-interpretable image features (HIFs). Our method leverages >1.6 million annotations from board-certified pathologists across >5700 samples to train deep learning models for cell and tissue classification that can exhaustively map whole-slide images at two and four micron-resolution. Cell- and tissue-type model outputs are combined into 607 HIFs that quantify specific and biologically-relevant characteristics across five cancer types. We demonstrate that these HIFs correlate with well-known markers of the tumor microenvironment and can predict diverse molecular signatures, including expression of four immune checkpoint proteins and homologous recombination deficiency, with performance comparable to ‘black-box’ methods. Our approach provides a comprehensive, quantitative, and interpretable window into the composition and spatial architecture of the tumor microenvironment.
Watch the seminar by clicking here.
Dr Korsuk Sirinukunwattana
University of Oxford, Oxford, UK (click here for a short bio)
19 Dec 2022
Title: Image-based consensus molecular subtypes of colorectal cancer
Abstract: In colorectal cancer (CRC), conventional histological grading is of little clinical value as it does not provide an insight into genetic/molecular events that drive the disease outcome. Image analysis of tumour tissue slides generated during a diagnostic examination of patients is a cost-effective tool to associate complex features of tissue organisation of CRC tumours with molecular and outcome data. Here we predict consensus molecular subtypes (CMS) of CRC from digital whole-slide images of CRC tissue sections using an ensemble of deep convolutional neural networks (Inception V3). Domain adversarial training and validation of the neural networks were performed using 1,553 tissue sections with comprehensive multi-omic data from three independent datasets (FOCUS trial, n=362 stage IV CRC; rectal cancer biopsies, n=175; The Cancer Genome Atlas (TCGA), n=572). Image-based consensus molecular subtyping (imCMS) accurately classified CRC whole-slide images and preoperative biopsies and spatially resolved intratumoural heterogeneity. In all three cohorts, imCMS reproduced expected correlations with (epi)genomic alterations and effectively stratified patients into prognostic subgroups. Leveraging artificial intelligence for the development of novel biomarkers extracted from histological slides with molecular and biological interpretability has remarkable potential for clinical translation.
Watch the seminar by clicking here.
Spring 2023
Dr André Homeyer
Fraunhofer MEVIS, Bremen, Germany (click here for a short bio)
16 Jan 2023
Title: Artificial Intelligence in Pathology: From Prototype to Product
Abstract: Despite thousands of published research papers on applications of artificial intelligence (AI) in pathology, only few prototypes have matured into commercial products for routine use. This talk will provide a brief overview and advice on various challenges that must be overcome before an AI solution can be brought to market. Topics covered include integration into the laboratory IT infrastructure, business models and reimbursement options. The talk will also address how to obtain regulatory approval for in vitro diagnostic medical devices and how to compile appropriate test datasets to evaluate real-world performance.
Watch the seminar by clicking here.
Sarah Fremond
Leiden University Medical Center, Leiden, Netherlands (click here for a short bio)
6 Feb 2023
Title: Interpretable deep learning model to predict the molecular classification of endometrial cancer from H&E-stained whole-slide images: a combined analysis of the PORTEC randomised trials and clinical cohorts
Abstract: Endometrial cancer can be molecularly classified into POLEmut, mismatch repair deficient (MMRd), p53 abnormal (p53abn), and no specific molecular profile (NSMP) subgroups. We aimed to develop an interpretable deep learning pipeline for whole-slide-image-based prediction of the four molecular classes in endometrial cancer (im4MEC), to identify morpho-molecular correlates, and to refine prognostication. im4MEC was trained to learn tile-level morphological features with self-supervised learning and to molecularly classify whole-slide images with an attention mechanism. Predictions of a nuclear classification deep learning model serve to derive interpretable morphological features. im4MEC attained macro-average AUROCs of 0,874 (95% CI 0,856–0,893) on four-fold cross-validation and 0,876 on the independent test set, PORTEC-3 a randomized trial of high risk patients. 5-year recurrence-free survival by im4MEC predicted molecular classes significantly stratified patients from PORTEC-3 (log-rank p<0,0001). POLEmut and MMRd tiles had a high density of lymphocytes, p53abn tiles had strong nuclear atypia, and NSMP had a low tumour-to-stroma ratio, a potentially novel characteristic feature.
Oliver Lester Saldanha
Uniklinik RWTH Aachen, Aachen, Germany (click here for a short bio)
20 Feb 2023
Title: Swarm learning for decentralized deep learning in cancer histopathology
Abstract: A limitation in computational pathology is the difficulty of exchanging data. Swarm Learning (SL) is a decentralized protocol for training deep learning models. In this study, we evaluated the effectiveness of SL in predicting microsatellite instability (MSI) and BRAF mutation status from histopathology images of colorectal and gastric cancers. We successfully used SL on large, multi-centric datasets of gigapixel histopathology images from more than 5,000 patients. Our results demonstrate that AI models trained using SL can accurately predict the BRAF mutational status and microsatellite instability directly from Hematoxylin and Eosin (H&E) stained pathology slides of colorectal and gastric cancers. It is crucial to have large datasets in the computational pathology of cancer, ideally from multiple centres, to minimize bias. However, the collection of such datasets faces practical, ethical, and legal obstacles. SL provides a potential solution to overcome these obstacles and may serve as an alternative for sharing patient-related data across various locations in the future.
Watch the seminar by clicking here.
Prof Marc Aubreville
Technische Hochschule Ingolstadt, Ingolstadt, Germany (click here for a short bio)
6 Mar 2023
Title: Mitosis Detection in Arbitrary Tumor Tissue - is the problem solved?
Abstract: The frequency of cells dividing, known as mitotic count, is strongly related to tumor growth and survival rates. However, assessing mitotic count is subjective and challenging for human experts, particularly identifying the most prognostic area with the highest mitotic count. Consequently, automatic detection of mitotic figures using computer vision has gained attention. The Mitosis Domain Generalization (MIDOG) challenges evaluate the quality of methods for detection in datasets that may differ significantly in appearance due to variations between tumor subtypes or imaging centers. Several domain generalization methods have been tested in these challenges, with many surpassing human performance. Nevertheless, despite achieving superhuman performance, we still need to identify and explore factors that may limit robustness in real-world scenarios. Therefore, this talk aims to investigate unexplored factors that may affect detection accuracy, even with advanced methods in place.
Watch the seminar by clicking here.
Dr Hanya Mahmood
University of Sheffield, Sheffield, UK (click here for a short bio)
20 March 2023
Title: Prediction of malignant transformation and recurrence of oral epithelial dysplasia using architectural and cytological feature specific prognostic models
Abstract: Oral epithelial dysplasia (OED) is a precursor state usually preceding oral squamous cell carcinoma. Histological grading is the gold standard for OED prognostication but is subjective with inconsistent outcome prediction. This talk will present the findings from a recently published study, which explores if individual OED histological features can be used to develop predictive models for malignant transformation and recurrence. Two models will be discussed: a ‘6-point model’ using the six features showing greatest prognostic association and a ‘two-point model’ using the two features with highest inter-rater pathologist agreement. Both models showed good predictive ability for both transformation and recurrence, with further improvements when certain clinical variables were added. The findings demonstrate a correlation between individual OED histological features and prognosis for the first time. The proposed models have the potential to simplify OED grading and improve patient management.
Watch the seminar by clicking here.
Paul Tourniaire
Université Côte d'Azur, Nice, France (click here for a short bio)
18 April 2023
Title: MS-CLAM - A Proof-of-Concept for mixed supervision in digital pathology
Abstract: Given the size of digitized Whole Slide Images (WSIs), it is generally laborious and time-consuming for pathologists to exhaustively delineate objects within them, especially with datasets containing hundreds of slides to annotate. Most of the time, only slide-level labels are available, which has lead to the development of weakly-supervised models. However, it is often difficult to obtain from such models accurate object localization, e.g., patches with tumor cells in a tumor detection task, as they are mainly designed for slide-level classification. In this talk, I will introduce the concept of mixed supervision for histopathology, a supervision paradigm where multiple levels of supervision are used simultaneously for a single or multiple tasks. Then, I will explain how mixed supervision was implemented in MS-CLAM, a model that was adapted from a well-established weakly-supervised, attention-based Multiple Instance Learning model designed for WSI classification. Finally, I will show on two different datasets how including a few strongly annotated slides with a bigger weakly annotated set can bring notable improvements in terms of classification, but most importantly in terms of localization.
Watch the seminar by clicking here.
Summer 2023
Prof Azra Raza
Columbia University Medical Center, New York, USA (click here for a short bio)
15 May 2023
This is a special edition of our TIA Centre Seminar Series, held in collaboration with the TIA Centre, Health GRP and Warwick Cancer Centre
Title: THE FIRST CELL and the human costs of pursuing cancer to the last
Abstract: Cancer will strike 1 in 2 men and 1 in 3 women. Yet, we are failing spectacularly to improve outcome for the majority of patients. Our contention is that the real solution to the cancer problem is to diagnose cancer early, at the stage of The First Cell. The footprints of early cancers, the surrogate biomarkers, need to be identified so we can detect the first cell. The rapidly evolving technologies are doing much in this area but need to be expanded. We study a pre-leukemic condition called myelodysplastic syndrome (MDS) with the hope that we can detect the first leukemia cells as the disease transforms to acute myeloid leukemia (AML). Towards this end, we have collected blood and bone marrow samples on MDS and AML patients since 1984. Today, our Tissue Repository has more than 60,000 samples. We propose novel methods to identify surrogate markers that can identify the First Cell through studying the serial samples of patients who evolve from MDS to AML.
Watch the seminar by clicking here.
Dr Germán Corredor (click here for a short bio)
Emory University, Atlanta, Georgia, USA
19 June 2023
Title: Spatial connectivity of tumor and associated cells (SpaCell): a novel computational pathology biomarker
Abstract: The tumor microenvironment is comprised of multiple cell types, with their spatial organization having been previously studied to identify associations with disease progression and response to therapy. These works, however, have focused on spatial interactions of a single cell type, ignoring spatial interplay between the remaining cells. Here, we introduce a framework to quantify complex spatial interactions on H&E-stained image between multiple cell families simultaneously within the microenvironment, called spatial connectivity of tumor and associated cells (SpaCell). First, nuclei are segmented and classified into different families (e.g., cancerous cells and lymphocytes) using a combination of image processing and machine learning techniques. Local clusters of proximal nuclei are then built for each family. Next, quantitative metrics are extracted from these clusters to capture inter- and intra-family relationships, namely: density of clusters, area intersected between clusters, diversity of clusters surrounding a cluster, architecture of clusters, among others. When evaluated for predicting risk of recurrence in HPV-associated oropharyngeal squamous cell carcinoma and non-small cell lung cancer, SpaCell was able to differentiate between patients at high and low risk of recurrence.
Watch the seminar by clicking here.
Prof John Le Quesne
University of Glasgow, Glasgow, UK (click here for a short bio)
17 July 2023
Title: Self-supervised learning in lung cancer histopathology
Abstract: Lung adenocarcinoma diagnosis, prognosis, and therapy choices are all governed by expert judgement of histological images. Artificial intelligence (AI) methods to assist human pathologists are rapidly becoming accepted, though not yet clinically deployed in lung cancer. We present a novel self-supervised Histomorphological Phenotype Learning (HPL) method which is trained to identify and quantify the underlying 'landscape' of tumour morphology without the use of any expert annotations. The histomorphological phenotype clusters (HPCs) that it discovers represent recurrent naturally selected modes of tumour growth worthy of study in their own right. Furthermore, they immediately recapitulate established knowledge of high risk patterns, demonstrate the prognostic importance of TILs, and generate novel biological hypotheses. This method has the potential to generate authoritative 'foundation models' with enormous value as assistance in diagnosis, prognosis, and prediction.
Watch the seminar by clicking here.
Dr Mahdi S. Hosseini
Concordia University, Montreal, Canada (click here for a short bio)
19 July 2023
Title: Computational Pathology: The Current Trend and Future Challenges
Abstract: The computational advantages of deep learning in AI, integrated with digital pathology for microscopy imaging, has led to the emergence of a new field called Computational Pathology (CoPath) that is poised to transform clinical pathology globally. The field of CoPath is dedicated to the creation of automated tools that address and aid steps in the clinical workflow for diagnosing and treatment of cancer diseases. With increasing advancements in deep learning, image analytics, and enabling hardware, the research focus in this field has expanded and branched into a broad range of domains. In this seminar we present the ongoing research trends in deep learning and computer vision applied in computational pathology. We investigate this from application-centric, data-centric and model-centric viewpoints to cohesively relate between “clinical application”, “data preparation” and “learning-models” for effective AI designs in decision making algorithms. From application centric viewpoint we discuss the importance of steps in clinical pathology in relation to the computational approaches for automation. From data-centric viewpoint we discuss the merits in data annotation and processing from weakly-supervised to fully-supervised approaches. Finally, from model-centric viewpoint, we discuss the advances in representation learning models for computational pathology and their diagnostic applications in clinical problems. We conclude this talk with the objectives of discussing challenges in clinics and research for future AI developments in CoPath and how they can facilitate the transformational changes in clinical pathology for cancer diagnostics.
Watch the seminar by clicking here.
Dr Andrey Bychkov
Kameda Medical Center, Kamogawa, Japan (click here for a short bio)
24 July 2023
Title: Digital and Computational Pathology in Asia: A Navigation Guide
Abstract: Asia is the most populous continent and has the highest burden of total cancer cases that require adequate diagnosis and treatment. However, many countries lack the necessary resources to meet this demand. Digital and computational pathology is expected to bridge the gap in the diagnostic domain; however, its adoption in Asian countries has been slower compared to Western countries. This presentation will give an overview of the recent developments and achievements of DP-AI in various Asian regions, from Japan to India and from Southeast Asia to the Arabic world. It will also discuss the industry and startups involved in this field. Join us for a quick tour of Asia and discover how DP-AI is transforming pathology across the continent.
Prof Jason Swedlow
University of Dundee, Dundee, UK (click here for a short bio)
21 August 2023
Title: Making BioImage Data FAIR on a Global Scale: OME’s Bio-Formats, OME-TIFF, OMERO, & IDR
Abstract: Despite significant advances in biological imaging and analysis, major informatics challenges remain unsolved: file formats are proprietary, storage and analysis facilities are lacking, as are standards for sharing image data and results. The Open Microscopy Environment (OME; http://openmicroscopy.org) is an open-source software framework which aims to address these challenges. OME releases specifications and software for managing image datasets and integrating them with other scientific data. OME’s Bio-Formats and OMERO are used in 1000’s of labs worldwide to enable discovery with imaging. OME-TIFF is an open, metadata-rich, multi-dimensional, multi-resolution data format for modern bioimaging that has been widely adopted across the bioimaging community. OME-NGFF is a new file format designed for cloud-based data resources. We have used Bio-Formats and OMERO to build solutions for sharing and publishing imaging data. The Image Data Resource (IDR; https://idr.openmicroscopy.org) includes >320 TBytes of image data linked to >120 independent studies from genetic, RNAi, chemical, localisation and geographic high content screens, super-resolution microscopy, single cell profiling, light sheet microscopy of developing organisms and tissues, and digital pathology. Datasets range from several GBs to tens of TBs. Wherever possible, we have integrated image data with all relevant experimental, imaging and analytic metadata. These annotations make it possible to re-use IDR data, and to connect independent imaging datasets by molecular perturbations and phenotypes. IDR is part of a global effort to build public bioimage data resources and linked to EMBL-EBI’s BioImage Archive and RIKEN’s Systems Science of Biological Dynamics database. We are currently testing whether OME-NGFF can be used to enable federated data resources so that IDR can become a globally connected data publishing system.
Watch the seminar by clicking here.