Cambridge MedAI Seminar Series

"AI in Histopathology: Practical Lessons from an Unconventional Case Study" and "Next generation technology for next generation trials"

This month’s seminar will be held on Wednesday 29 January 2025, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

Click here to register.

A light lunch from Aromi will be served from 11:45.

The event will feature the following talks:

AI in Histopathology: Practical Lessons from an Unconventional Case Study – Greta Markert, PhD student, Cancer Research UK Cambridge Institute/University of Cambridge

Greta studied both Chemistry and Pharmaceutical Sciences at ETH Zurich with a strong focus on computational approaches. During her master’s thesis at IBM Research, she explored AI for drug discovery, which sparked her passion for artificial intelligence. Currently, she is in the final year of her PhD at the Cancer Institute in Cambridge, working under Prof. Florian Markowetz and Prof. Rebecca Fitzgerald at the Early Cancer Institute, working on AI in histopathology. Before starting her PhD, she worked in management consulting and in the patents department of Roche.

Abstract: Artificial intelligence in histopathology has predominantly focused on traditional biopsy samples. My research, however, applies AI to whole slide images derived from the capsule sponge, a minimally invasive alternative to endoscopy. The capsule sponge collects random cellular material along the esophagus, presenting distinct analytical challenges. Our work involves three stains—H&E for quality control and atypical features, TFF3 for detecting Barrett’s esophagus, and TP53 for tumor progression assessment—each addressing specific diagnostic questions. By integrating these stains and analyzing corresponding cellular structures, we enhance risk stratification and advance early detection of esophageal cancer. This presentation will outline the novel computational strategies developed to tackle this unique and complex application.

Next generation technology for next generation trials – Dr Karen Sayal, Senior Director in AI-driven Clinical Development and Clinical Translation at Recursion Pharmaceuticals & Honorary Consultant in Clinical Oncology, Cambridge University NHS Foundation Trust

Dr Karen Sayal is a Senior Director in AI-driven Clinical Development and Clinical Translation at Recursion Pharmaceuticals where she is focused on implementing high-throughput industrial scale clinical trials. She is also a honorary consultant in Clinical Oncology at Cambridge University NHS Foundation Trust.

Dr Sayal completed medical school at the University of Cambridge (Gonville and Caius college). Her specialist clinical training spanned across Cambridge and Oxford, which included being the first NIHR-funded academic clinical fellow in Clinical Oncology at Oxford. She completed a CRUK-funded DPhil in advanced sequencing technologies and machine learning at the University of Oxford. Prior to joining Recursion, Dr Sayal was a Fellow in Deep Learning in the AI/ML division of GSK. She is the first and only clinician to have embarked on the GSK AI fellowship scheme where she worked across technical AI research, clinical trial design, clinical data networks and AI regulation.

Abstract: Clinical trials are being transformed through an evolving suite of innovative AI-driven technologies combined with data-driven insights on the clinical and biological profile of disease. Such transformation is a reflection of a more fundamental shift where technology, drug development and patient care are coming together to redefine how we view and manage perturbed states of human physiology. In the next Cambridge MedAI seminar, Dr Karen Sayal will give an overview of the current landscape of trial-ready AI tools. She will also spotlight emerging growth areas for clinical AI technologies, and offer critical insights into the challenges we must collectively address to ensure AI innovation is deployed in a safe and meaningful way for patients.

"AI_PREMie: Transforming Preeclampsia Care with the Power of SAS Viya" and "Machine learning for detecting and risk-stratifying clonal haematopoiesis "

Join us for the Cambridge AI in Medicine Seminar Series, hosted by the Cancer Research UK Cambridge Centre and the Department of Radiology at Addenbrooke’s. This series brings together leading experts to explore cutting-edge AI applications in healthcare—from disease diagnosis to drug discovery. It’s a unique opportunity for researchers, practitioners, and students to stay at the forefront of AI innovations and engage in discussions shaping the future of AI in healthcare.

This month’s seminar will be a joint event supported by SAS, the Maxwell Centre and the Office for Translational Research. It follows the recent announcement of the “Cambridge and SAS launch partnership in AI and advanced analytics to accelerate innovation in the healthcare sector.”

This month’s event will be held on 12 November 2024, 4-6pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom. This month will feature the following talks:

Introduction to SAS: Lisa Murch, Sr Industry Consultant, Global Health and Life Sciences Customer Advisory, SAS
AI_PREMie: Transforming Preeclampsia Care with the Power of SAS Viya: Ms Ana Le Chevillier, Manager of the AI Healthcare Hub at University College Dublin and Clinical Research Manager for Data and AI for AI_PREMie
Machine learning for detecting and risk-stratifying clonal haematopoiesis: Dr William Dunn (CRUK Clinical Research Fellow) and Dr Muxin Gu (Research Associate), Cambridge Stem Cell Institute, University of Cambridge

This will be followed by a networking session over drinks and snacks, with a demo on the SAS Viya platform: Applying Machine Learning and Artificial Intelligence in Real World Data in Personalized Medicine for Non-Small Cell Lung Cancer Patients.

For more information, sign-up, and Zoom details, see: https://www.eventbrite.co.uk/e/cambridge-medai-seminar-series-tickets-1070412164599

"Generative AI in Radiotherapy – Gimmick or Game Changer?" and "Utilising AI for outcome prediction in glioma surgery: challenges and opportunities"

This month’s seminar will be a joint event with the Brain Cancer Virtual Institute (BCVI), an institute within the CRUK Cambridge Centre. The BCVI has evolved from the CRUKCC Neuro-Oncology Programme and is a network of researchers and clinicians in Cambridge working to improve outcomes for brain tumour patients.

The event will be held on 29 October 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

A light lunch from Aromi will be served from 11:45.

Please sign up on Eventbrite.

This month will feature the following talks:

Generative AI in Radiotherapy – Gimmick or Game Changer? – Prof. Raj Jena, Professor of AI in Radiation Oncology and Honorary Consultant in Clinical Oncology, University of Cambridge Department of Oncology and Cambridge University Hospitals

Raj Jena is a Professor of AI in Radiation Oncology based at the University of Cambridge Department of Oncology and Cambridge University Hospitals. His research interests focus on clinical image processing, data science and machine learning applications. Raj is the chief Investigator for Hamlet.rt, a multi-centre radiomics study in radiation therapy open at over 12 sites over the UK and Tata medical centre in Kolkata. He is a member of the Royal College of Radiologists’ AI in Clinical Oncology (AICO) committee and is Director of the Oncology Translational Research Collaboration (O-TRC) at the National Institute for Healthcare Research.

Raj enjoys working at the interface between the clinical, academic and commercial sectors. Following successful collaborations with Siemens and other imaging companies, he worked a clinical consultant to the InnerEye team at Microsoft Research. Here Raj had the opportunity to work with thought leaders in medical image analysis, and subsequently led the NHS AI lab funded OSAIRIS project, which developed the first cloud based open source imaging AI solution to be deployed at Addenbrooke’s Hospital.

Raj is now applying his knowledge of machine learning and image processing to the STELLA project, an international collaboration developing a novel smart radiotherapy unit for low- and middle-income countries.

Abstract: Generative AI models have demonstrated amazing capabilities in the creation of synthetic data, but how useful can they be in discovery science? I will discuss the application of a generative machine learning model to a problem relating to modelling late effects of radiotherapy in the brain.

Utilising AI for outcome prediction in glioma surgery: challenges and opportunities – Yizhou Wan, Clinical Research Fellow and Honorary Speciality Registrar, Brain Tumour Imaging Lab, Division of Neurosurgery, Department of Clinical Neurosciences, University of Cambridge

Yizhou Wan is a Neurosurgery Resident with an interest in how we can use advanced imaging methods to detect tumour effects on the brain and optimise surgical and non-surgical treatments for brain cancer. His PhD studies the impact of surgery on cognition and tumour networks using advanced neuroimaging. Prior to starting doctoral training at the University of Cambridge, Yizhou studied Medicine at Imperial College London followed by Academic Foundation Training in London and Neurosurgery training in Oxford. His research is funded by a Cancer Research U.K, Clinical Research Training Fellowship and Royal College of Surgeons England Research Fellowship.

Abstract: High-grade glioma is the most common primary brain cancer. Cognitive symptoms are the commonest neurological deficits reported by patients. They have a significant impact on quality of life and reduce survival. Cognition depends on brain network functioning. Gliomas have been shown to integrate into neuronal circuits and disrupt whole-brain connectivity. Tumours interact with brain networks to disrupt cognition and promote tumour growth. My PhD studies the impact of surgery on cognition and tumour networks. By using diffusion MRI we can identify imaging markers associated with tumour-related injury and computationally model the effects of surgery on brain function. I hope to predict preoperatively the effect of surgery on postoperative cognition and survival, by treating glioma as a whole-brain disease. This will facilitate shared-decision making between surgeons and patients to formulate personalised resections which preserve maximise survival. I will also discuss the challenges involved in applying AI to clinical imaging datasets and current areas where we may be able to make progress in translating AI to the clinic and Operating room.

"Cascaded Transformer plus U-net in Medical Image Segmentation" and "Machine learning for treatment stratification in kidney cancer"

The next seminar will be held on 17 September 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

A light lunch from Aromi will be served from 11:50.

This month will feature the following two talks:

Cascaded Transformer plus U-net in Medical Image Segmentation – Dr Xin Du, Postdoctoral Research Associate, Department of Physics, University of Cambridge

Xin Du is a postdoctoral researcher in the RadNet data science team at the Cavendish Laboratory. She was a Ph.D. student at the University of Southampton, with research interests in information theory, Cascade Learning, and transfer learning with applications to problems in computer vision, biology, and human activity monitoring from wearable sensors. Xin’s work is aimed at developing new learning algorithms and architectures, and deeper understanding of them in the context of these applied problems. Currently, she is focusing on auto-segmentation of 3D medical images with deep learning and trying to develop a way to combine the information from both text descriptions and medical image contexts. Outside of research, she enjoys baking, travelling, meeting new people, and exploring new activities.

Abstract: Radiotherapy plays a crucial role in modern medicine but requires considerable time for manually contouring radio-sensitive organs at risk, which can delay treatment processing. With the significant success of deep convolutional neural networks, auto-segmentation in medical image analysis has shown substantial improvements in saving time and reducing inter-operator variability. While convolutional neural networks utilise the locality of convolution operations, they lose global and long-range semantic information. To address this, we propose a cascaded transformer U-net for medical image segmentation that compensates for long-range dependencies and mitigates computational requirements without compromising performance.

Machine learning for treatment stratification in kidney cancer – Rebecca Wray, PhD Student, Early Cancer Institute, University of Cambridge & Dr Hania Paverd, Clinical Research Training Fellow, Early Cancer Institute, University of Cambridge

Rebecca completed her undergraduate degree in Biosciences from Durham University, where she specialised in Biochemistry and Molecular Biology, before moving to Cambridge to join CS Genetics, a biotechnology start-up investigating novel single-cell RNA-sequencing methods. She then joined Dr Annie Speak’s group at the Cambridge Institute for Therapeutic Immunology and Infectious Disease (CITIID). Currently, Rebecca is in her second year of the prestigious Cancer Research UK (CRUK) Cambridge Centre MRes + PhD programme. Under the mentorship of Dr Mireia Crispin-Ortuzar and Dr James Jones, she is employing data-driven approaches to uncover novel biomarkers and mechanisms related to treatment failure and resistance in kidney cancer.

Hania is a medical doctor specialising in Radiology, with a research interest in machine learning for medical image analysis. She studied Medicine at Newnham College, University of Cambridge, before moving onto Specialty Training in Radiology at Addenbrooke’s Hospital. She is currently in her first year of PhD as a Clinical Research Training Fellow at the Early Cancer Institute in Cambridge, working under the supervision of Dr Mireia Crispin-Ortuzar and Dr Matthew Hoare. Her PhD research focuses on computational analysis of CT and MRI scans, integrated with other data modalities such as genomic data, to enhance risk stratification for patients with liver disease and improve early detection of liver cancer.

Abstract: Clear cell renal cell carcinoma (ccRCC) is the most lethal urological malignancy. The cancer is highly heterogeneous, and therapy response varies between patients. In a subset of cases, the tumour extends into the renal vein and inferior vena cava, termed venous tumour thrombus (VTT), which complicates surgical intervention. While response signatures have been developed for metastatic RCC, there’s a notable gap for patients with VTT. Here we present molecular analysis of data from NAXIVA, a single-arm Phase II study, where 35% of patients showed a reduction in VTT length in response to Axitinib, a tyrosine kinase inhibitor.

We develop a machine learning model which uses baseline and dynamic data taken from blood samples early in treatment, and demonstrates good patient stratification. We report novel biological markers for positive response to anti-angiogenic agents, including CCL17, IL-12p70, PlGF and Tie-2. This research paves the way for better patient stratification and response prediction, offering promising avenues for personalised therapy in ccRCC.

"How Low Can We Go? - Investigating the interaction between cancer-detecting AI and low-dose quantum noise in CT images", "Deformable registration to assess tumour progression in ovarian cancer patients2 and "Automating Segmentation and Chemotherapy Response Measurement in Ovarian Cancer with Multitask Deep Learning"

The next seminar will be held on 11 June 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

A light lunch from Aromi will be served from 11:50.

To sign up for this event please use this Eventbrite link

This month will feature the following talks:

How Low Can We Go? – Investigating the interaction between cancer-detecting AI and low-dose quantum noise in CT images – Jack Dixon, Master’s student, Department of Physics, University of Cambridge

Jack is an undergraduate student at the University of Cambridge currently studying for a Master’s Degree in Natural Sciences. He specialises in physics, and is particularly interested in statistical and computational physics. As part of his degree, he undertook a research project within the Early Cancer Institute under the supervision of William McGough, Dr Mireia Crispin-Ortuzar and Dr Ander Biguri, focused on low-dose CT scan simulation and deep-learning based segmentation.

Abstract: Renal cancers (RC) are associated with more than 140,000 deaths annually. Mortality rates for RC could be reduced if a suitable screening program to allow for early diagnosis was constructed. Trials into screening, such as the Yorkshire Kidney Screening Trial, use non-contrast enhanced CT scans and ideally seek to lower the dose of ionising radiation as much as is feasible. In order for a screening program to be effective, it must be both cost-effective and (relatively) safe. To this end, my Master’s project focused on assessing the performance of automatic renal segmentation models as the incident radiation dose of the input CT scans is decreased. This involved first attempting to construct and validate a low-dose CT scan simulation technique that can be applied retroactively, and then assessing segmentation performance as the dose is decreased. The renal and cancer segmentation models produced both displayed strong positive ranked correlation between Dice similarity coefficient and incident dose to a significance level of 2.5%. We conclude that renal segmentation performance in non-contrast enhanced CT scans is correlated with the incident dose.

Deformable registration to assess tumour progression in ovarian cancer patients – Gift Mungmeeprued – Master’s student, Department of Physics, University of Cambridge

Gift Mungmeeprued is a master’s student in the Department of Physics at the University of Cambridge. She is interested in machine learning to make healthcare more accessible and affordable.

Abstract: High-grade serous ovarian carcinoma (HGSOC) is the most common and deadliest subtype of ovarian cancer, often characterised by multi-site and heterogeneous tumours. The standard line of treatment for HGSOC in the UK is neoadjuvant chemotherapy (NACT) followed by delayed primary surgery. Response Evaluation Criteria in Solid Tumours (RECIST 1.1) is the current standardised criteria to assess the tumour response to NACT based on measurements of tumour diameters in pre- and post-NACT CT scans. While RECIST is designed to be relatively quick for radiologists to evaluate, it only captures 1-dimensional global change in tumour size. In this talk, we explored the use of deformable image registration as an automated tool to assess tumour response to NACT. Registration between pre- and post-NACT CT scans reveals spatial heterogeneity of changes within the tumour and across multiple disease sites.

Automating Segmentation and Chemotherapy Response Measurement in Ovarian Cancer with Multitask Deep Learning – Bevis Drury, Master’s student, Department of Physics, University of Cambridge

Bevis is a Part III student studying physics at the University of Cambridge. He is interested in applying machine learning to all areas of research, from physics to medicine.

Abstract: High Grade Serous Ovarian Cancer (HGSOC) is the most common type of ovarian cancer. Often diagnosed at advanced stages, HGSOC presents significant challenges due to its heterogeneity and metastatic nature. Treatment of HGSOC begins with either immediate primary surgery, or neoadjuvant chemotherapy prior to delayed primary surgery. To track disease progression, radiologists routinely use abdominopelvic CT imaging. The patient’s radiological response to treatment can be measured using the Response Evaluation Criteria in Solid Tumours (RECIST), which compares CT scans taken before and after treatment. Manual calculation of RECIST is time-consuming and often inconsistent between radiologists, impacting the accuracy and reliability of treatment assessments. This paper develops a multitask deep learning architecture for automating the segmentation and chemotherapy response prediction of HGSOC patients. The model combines features from two identical U-Net architectures, which are then used to predict binarised RECIST labels. We use a training cohort of 99 HGSOC cases with pre- and post-treatment CT scans, and an external validation cohort of 49 cases. For the validation cohort, we predict binarised RECIST labels with an AUC of 0.78. We are the first to predict RECIST labels for HGSOC patients using multitask deep learning, establishing this research as a benchmark for future work. RECIST measurements are not currently used in clinical practice, so this framework aims to provide radiologists with real-time segmentations and RECIST labels leading to more informed decisions.

"Big Data and AI in Cardiac Imaging - Making a difference?" and "Learning structures in multimodal pathology"

The next seminar will be held on 21 May 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

A light lunch from Aromi will be served from 11:50.

This month will feature the following two talks:

Big Data and AI in Cardiac Imaging – Making a difference? – Jonathan Weir-McCall, Assistant Professor, Department of Radiology, University of Cambridge

Jonathan is a University Lecturer at the University of Cambridge and an Honorary Consultant Cardiothoracic Radiologist at the Royal Papworth Hospital. His research interests lie in the use of cardiovascular CT and MRI for better understanding how these can be used to improve patient treatment and outcomes in structural and coronary artery disease. He has authored >130 peer reviewed publications, co-authored the SCCT guidelines on the role of CT in the assessment for transcatheter aortic valve insertion, and the BSCI/BSTI guidelines on the reporting of calcification on routine chest CT. He sits on the executive committee of the BSCI, guideline committee of the SCCT, and Diagnostic Advisory Committee of NICE.

Abstract: AI and advanced analytics are reaching clinical practice with significant opportunities, but also challenges in determining their real world impact and efficacy. In cardiac imaging, advanced analytics using AI and computational fluid dynamics are being routinely used in clinical care. While small scale randomised control trials present promising insights into their potential benefits, real world data is lacking. Leveraging national datasets we analyse the impact of these technologies in the UK, examining the impact of one AI-augmented CT tool on healthcare behaviours and patient outcomes.

Learning structures in multimodal pathology – Konstantin Hemker, PhD Candidate, Computer Laboratory, University of Cambridge

Konstantin is a PhD student in the Computer Lab at the University of Cambridge focussing on multimodal representation learning for biomedical data modalities. In particular, he is looking at how fusion models can provide multi-scale context in computational pathology. Before starting his PhD, Konstantin worked as a Senior Data Scientist in the Healthcare and Pharmaceuticals practice at the Boston Consulting Group, focussing on drug yield optimisation of active ingredients in antibody treatments and radiocontrast agents. He holds Master’s degrees in Computer Science from Imperial and Cambridge and an undergraduate degree from the London School of Economics.

Abstract: Integrative modelling of multiple data structures (such as images, graphs, sequences, or tabular data) in the same model is a common challenge for machine learning approaches in biomedical domains. This challenge arises from a lack of shared semantics between modalities, one-to-many relationships, missing modalities, and data sparsity. Meanwhile, multi-scale context can provide important information about the tumour microenvironment in fields such as computational pathology and consequently help train better predictive models. This talk will cover state-of-the-art multimodal representation learning methods that can learn from multiple data distributions, capture cross-modal relationships, and handle missing modalities whilst maintaining structural information from each modality for predictive tasks in pathology.

This is a hybrid event so you can also join via Zoom: https://zoom.us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09 Meeting ID: 990 5046 7573 and Passcode: 617729

Integrated Cancer Medicine and Cambridge MedAI Seminar: Multimodal AI for Radiology

Speaker: Stephanie Hyland, Principal Researcher, Microsoft Cambridge

Thursday 18 April, 15:00—17:00 in the Rayleigh seminar room, Maxwell Centre, JJ Thomson Avenue, Cambridge, CB3 0HE

The seminar will be followed by drinks and nibbles.

Please register to attend here (to facilitate catering).

For more information email: info@icm.cancer.cam.ac.uk

"Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings" and "Leveraging real-world histopathology datasets to inform clinical research"

This seminar will be held on Tuesday 26 March, 2:00-13:00 at the Jeffrey Cheah Biomedical Centre Main Lecture Theatre, University of Cambridge and streamed online via zoom.

To sign up on Eventbrite click here.

A light lunch will be provided from 11:50 from Aromi.

This month will feature the following two talks:

Robust and interpretable AI-guided marker for early dementia prediction in real-world clinical settings – Dr Delshad Vaghari, Research Associate at Department of Psychology, University of Cambridge

Predicting dementia early has major implications for clinical management and patient outcomes. Yet, we still lack sensitive tools for stratifying patients early, resulting in patients being undiagnosed or wrongly diagnosed. Despite rapid expansion in machine learning models for dementia prediction, limited model interpretability and generalizability impede translation to the clinic. We build a robust and interpretable predictive prognostic model (PPM) and validate its clinical utility using real-world, routinely-collected, non-invasive, and low-cost (structural MRI scan, cognitive scales) patient data. To enhance scalability and generalizability to the clinic, we: 1) train the PPM with clinically-relevant predictors (grey matter atrophy, clinical scales) that are common across research and clinical cohorts, 2) test PPM predictions with independent multicenter real-world data from memory clinics across countries (UK, Singapore). PPM robustly predicts whether patients at early disease stages (MCI) will remain stable or progress to Alzheimer’s Disease (AD). PPM generalizes from research to real-world patient data across memory clinics and its predictions are validated against longitudinal clinical outcomes. PPM allows us to derive an individualized AI-guided multimodal marker (i.e. predictive prognostic index) that predicts progression to AD more precisely than standard clinical markers (grey matter atrophy, cognitive scores) or clinical diagnosis, reducing misdiagnosis. Our results demonstrate a robust and explainable clinical AI-guided marker for early dementia prediction that is validated against longitudinal, multicenter patient data across countries, and has strong potential for translation to clinical settings.

Leveraging real-world histopathology datasets to inform clinical research – Irina Zhang, Data Scientist at AstraZeneca, Cambridge

Recent advances in Computational Pathology have demonstrated how we can benefit greatly from applying ML&AI to decipher giga-pixel whole-slide histopathology images. However, it is still incredibly difficult to generalise models developed on high-quality datasets to heterogeneous tissue samples collected in clinical settings. We have investigated various real-world evidence cohorts to address the inherent challenges of real-world histopathology images and develop interpretable and generalizable AI pipelines to inform our clinical research, with the perspective to apply advanced digital pathology to clinical settings and benefit patients in various therapeutic areas.

This is a hybrid event so you can also join via Zoom: Meeting ID: 990 5046 7573 and Passcode: 617729

Integrated Cancer Medicine and Cambridge MedAI Seminar: Interpretable AI for Precision Histopathology

Speaker: Professor Nasir Rajpoot, Professor of Computational Pathology & Director, Tissue Image Analytics (TIA) Centre, University of Warwick, UK

Thursday 29 February, 15:00—17:00 in the Rayleigh seminar room, Maxwell Centre, JJ Thomson Avenue, Cambridge, CB3 0HE

The seminar will be followed by drinks and nibbles.

Please register to attend here (to facilitate catering). For more information email: info@icm.cancer.cam.ac.uk

"Multimodal, data-efficient, and robust AI for real-world biosignals and the role of generative models" and "Using machine learning methods to improve classification and prediction of psychiatric conditions"

The Next seminar will be held on 30 January 2024, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lecture Theatre), University of Cambridge and streamed online via Zoom.

To sign up on Eventbrite click here.

This month will feature the following two talks:

Multimodal, data-efficient, and robust AI for real-world biosignals and the role of generative models – Dr Dimitris Spathis, Senior Research Scientist at Nokia Bell Labs and Visiting Researcher at University of Cambridge

The limited availability of labels for machine learning on multimodal data hampers progress in the field. In this talk, I will discuss our recent efforts to address this problem, building on the paradigms of self-supervised and multimodal learning. With models such as CroSSL, Step2Heart, and SelfHAR, we put forward principled ways to learn generalizable representations from high-resolution data through masking, knowledge distillation, and physiology-inspired pre-training. We show that these models can be applied to various clinically relevant applications to improve mental health, fitness, sleep, and voice-based diagnostics. At the same time, due to data size limitations, these models are limited in size and generalization capabilities compared to popular generative models such as GPT. What if we could use Large Language Models (LLMs) as data-agnostic pre-trained models? I will close the talk by highlighting where LLMs fail in processing sequential data as text tokens and some ideas on how to address the critical “modality gap”.

Using machine learning methods to improve classification and prediction of psychiatric conditions – Dr Katharina Zühlsdorff, Visiting Postdoctoral Fellow at Department of Psychology

Cognitive flexibility can be investigated using tests such as probabilistic reversal learning (PRL). In various neuropsychiatric conditions, including substance use disorders, gambling disorder, major depressive disorder and schizophrenia, overall impairments in PRL flexibility are observed. Using reinforcement learning (RL) models, a deeper mechanistic explanation of the latent processes underlying flexibility can be gained. I will present results from an analysis of PRL data from individuals with different psychiatric diagnoses using a hierarchical Bayesian RL approach and relate behavioural findings to the underlying neural substrates. Furthermore, I will discuss how graph neural network models can be used to incorporate cognitive and neuroimaging data to improve prediction of psychiatric conditions.

This is a hybrid event so you can also join via Zoom: Meeting ID: 990 5046 7573 and Passcode: 617729