Seminars 24/25
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 2023-24 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 a particular seminar below, or even giving a seminar. Alternatively, please fill-out the following form if you are interested in registering for the seminar series mailing list TIA Seminar Series Registration.
Note. We will continue to update this page with more details about the upcoming seminars.
Spring 2025
| Date | Speaker | Title |
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13th January 2025 |
TBC |
TBC |
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20th January 2025 |
Lee Cooper Northwestern Medicine |
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3rd February 2025 |
Kun-Hsing Yu Harvard Medical School |
A pathology foundation model for cancer diagnosis and prognosis prediction |
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TBC 2025 |
Danielle Belgrave GSK |
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TBC 2025 |
Max Lu Harvard Medical School |
Autumn 2024
| Date | Speaker | Title |
|
21st October 2024 |
Peter Neidlinger Technical University Dresden |
Benchmarking foundation models as feature extractors for weakly-supervised computational pathology |
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4th November 2024 |
Jack Breen University of Leeds |
Foundation Models and Multiple Instance Learning Methods for Ovarian Cancer Subtype Classification |
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11th November 2024 |
Nicholas Trahearn Institute of Cancer Research |
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25th November 2024 |
Spencer Watson University of Lausanne |
Investigating Glioblastoma Recurrence with Spatial Multi-Omics |
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9th December 2024 |
Hanwen Xu University of Washington |
A whole-slide foundation model for digital pathology from real-world data |
Autumn 2024
Peter Neidlinger
Technical University Dresden, Dresden, Germany (click here for a short bio)
21 Oct 2024
Title: Benchmarking Foundation Models as Feature Extractors for Weakly-Supervised Computational Pathology
Abstract: Advancements in artificial intelligence have driven the development of numerous pathology foundation models capable of extracting clinically relevant information. However, there is currently limited literature independently evaluating these foundation models on truly external cohorts and clinically relevant tasks to uncover adjustments for future improvements. In this study, we benchmarked ten histopathology foundation models on 13 patient cohorts with 6,791 patients and 9,493 slides from lung, colorectal, gastric, and breast cancers. The models were evaluated on weakly-supervised tasks related to biomarkers, morphological properties, and prognostic outcomes. We show that a vision-language foundation model, CONCH, yielded the highest performance in 42% of tasks when compared to vision-only foundation models. The experiments reveal that foundation models trained on distinct cohorts learn complementary features to predict the same label and can be fused to outperform the current state of the art. Creating an ensemble of complementary foundation models outperformed CONCH in 66% of tasks. Moreover, our findings suggest that data diversity outweighs data volume for foundation models. Our work highlights actionable adjustments to improve pathology foundation models.
Watch the seminar by clicking here.
Jack Breen
University of Leeds, Leeds, UK (click here for a short bio)
4 Nov 2024
Title: Foundation Models and Multiple Instance Learning Methods for Ovarian Cancer Subtype Classification
Abstract: Ovarian cancer histological subtyping is a vital diagnostic task as the subtypes represent vastly different diseases with varied treatment options and prognoses. It has previously been an underrepresented task in AI subtyping research. In this talk, I will discuss my thesis "Artificial Intelligence for Ovarian Cancer Diagnosis from Digital Pathology Slides", which focuses on the robust validation of multiple instance learning (MIL) subtyping models, and explores factors limiting the clinical utility of these models. Histopathology foundation models show great promise across many tasks, but analyses have typically been limited by arbitrary hyperparameters that were not tuned to the specific task - we recently reported the most rigorous single-task validation of histopathology foundation models to date (https://arxiv.org/abs/2405.09990). We have also explored the effects of replacing standard MIL methods with a multi-resolution graph network (https://arxiv.org/abs/2407.18105), alongside earlier studies focused on the computational efficiency of classification. Our other work has included investigating the effects of chemotherapy on morphological subtyping, predicting treatment response effectiveness, and the automated detection of metastases in the lymph nodes and omentum. The modern classifiers have drastically improved diagnostic performance, though some vital hurdles still need to be cleared for these models to achieve clinical utility.
Watch the seminar by clicking here.
Dr Nicholas Trahearn
Institute of Cancer Research, London, UK (click here for a short bio)
11 Nov 2024
Title: Investigating the Spatial Diversity of the Prostate Cancer Microenvironment using Artificial Intelligence
Abstract: Current prostate cancer risk predictors are not able to fully capture a patient’s risk of recurrence at the time of diagnosis, with cure rates varying substantially between patients in the same risk category, and is particularly challenging for the broad range of patients assessed as “high-risk”. In this talk I will discuss work from our recent Nature Cancer paper (https://doi.org/10.1038/s43018-024-00787-0), which assesses the impact of newly derived cancer evolution metrics as predictors of outcome within a clinical trial of high-risk prostate cancer patients. Patients enrolled on this trial received a combination of androgen deprivation therapy (ADT) and intensity-modulated radiotherapy (IMRT), and have received a median of 12 years of post-treatment follow-up. Using over 600 FFPE blocks from patients enrolled on the trial, we generated a large dataset of matched histology and whole genome sequencing to test our evolutionary metrics. Through Artificial Intelligence based Digital Pathology tools, we characterised the tumour microenvironment, at diagnosis, on a per-gland and cellular level. From this, we saw that our AI-derived Gleason grading was able to outperform that of the trial pathologist, raising interesting questions regarding current grading guidelines. Furthermore, our new measure of morphological evolvability was shown to be an independent predictor of prostate cancer recurrence and metastasis, events that would occur up to a decade later. Remarkably, these patterns were also reflected in our analysis of genomic evolvability and, when combined, produced a prediction of patient outcome that was stronger than the sum of its parts. By encapsulating different aspects of cancer evolution at different scales, this study demonstrates the added value of a multi-modal strategy to prostate cancer risk prediction.
Watch the seminar by clicking here.
Dr Spencer Watson
University of Lausanne, Lausanne, Switzerland (click here for a short bio)
25 Nov 2024
Title: Investigating Glioblastoma Recurrence with Spatial Multi-Omics
Abstract: Glioblastoma recurrence is currently inevitable despite advances in standard-of-care treatment. An alternative approach of targeting the tumor microenvironment, specifically tumor-associated macrophages via CSF-1R inhibition was found to dramatically regress established tumors in preclinical trials. While tumor regression was sustained in ~50% of individuals, recurrent tumors emerged over time in the remaining subset. These recurrences were ubiquitously associated with fibrotic scars that had formed during treatment. This same fibrotic response to treatment was also observed following surgical resection, radiotherapy, and in patient recurrence samples. Investigating the complex evolving post-treatment tumor landscape required integrating multiple -omics approaches with new spatially resolved transcriptomics. Spatial multi-omics analyses of the post-treatment tumor microenvironment identified specific fibrotic domains as pro-tumor survival niches that encapsulated surviving glioma cells, which inhibited immune surveillance while maintaining the tumor cells in a dormant state. Integrated analyses revealed specific pathways in the initiation of fibrosis that could be targeted in combination with anti-glioma therapy. These combinatorial therapies inhibited treatment-associated fibrosis, and significantly improved survival in preclinical trials of anti-CSF-1R therapy.
Hanwen Xu
University of Washington, Seattle, USA
9 Dec 2024
Title: A whole-slide foundation model for digital pathology from real-world data
Abstract: TBC
Spring 2025
Prof Lee Cooper
Northwestern Medicine, Chicago, USA
20 Jan 2025
Title: TBC
Abstract: TBC
Prof Kun-Hsing Yu
Harvard Medical School, Boston, USA
3 Feb 2025
Title: A pathology foundation model for cancer diagnosis and prognosis prediction
Abstract: TBC
Dr Danielle Belgrave
GSK, London, UK
TBC 2025
Title: TBC
Abstract: TBC
Dr Ming Lu
Harvard Medical School, Boston, USA
TBC 2025
Title: A Multimodal Generative AI Copilot for Human Pathology
Abstract: TBC