BEGIN:VCALENDAR VERSION:2.0 PRODID:-//Talks.cam//talks.cam.ac.uk// X-WR-CALNAME:Talks.cam BEGIN:VEVENT SUMMARY:Cambridge MedAI Seminar Series - Speaker to be confirmed DTSTART:20240904T110000Z DTEND:20240904T120000Z UID:TALK220756@talks.cam.ac.uk CONTACT:Ines Machado DESCRIPTION:The *Cancer Research UK Cambridge Centre* and the *Department of Radiology at Addenbrooke's* are pleased to announce a seminar series on *Artificial Intelligence (AI) in Medicine*\, which aims to provide a comp rehensive overview of the latest developments in this rapidly evolving fie ld. As AI continues to revolutionize healthcare\, we believe it is essenti al to explore its potential and discuss the challenges and opportunities i t presents.\n\nThe seminar series will feature prominent experts in the fi eld who will share their research and insights on a range of topics\, incl uding AI applications in disease diagnosis\, drug discovery\, and patient care. Each seminar will involve two/three talks\, followed by an interacti ve discussion with a light lunch from Aromi! We hope that this seminar ser ies will be a valuable platform for researchers\, practitioners and studen ts to learn about the latest trends and explore collaborations in the exci ting field of AI in Medicine.\n\n\n\nThe next seminar will be held on *17 September 2024\, 12-1pm at the Jeffrey Cheah Biomedical Centre (Main Lectu re Theatre)\, University of Cambridge* and streamed online via Zoom. A lig ht *lunch from Aromi will be served from 11:50*. This month will feature t he following talks:\n\n\n\n*Cascaded Transformer plus Unet in Medical Imag e Segmentation - Dr Xin Du\, Postdoctoral Research Associate\, Department of Physics\, University of Cambridge*\n\nXin Du is a postdoctoral research er in the RadNet data science team at the Cavendish Laboratory. She was a Ph.D. student at the University of Southampton\, with research interests i n information theory\, Cascade Learning\, and transfer learning with appli cations to problems in computer vision\, biology\, and human activity moni toring from wearable sensors. Xin’s work is aimed at developing new lear ning algorithms and architectures\, and deeper understanding of them in th e context of these applied problems. Currently\, she is focusing on auto-s egmentation of 3D medical images with deep learning and trying to develop a way to combine the information from both text descriptions and medical i mage contexts. Outside of research\, she enjoys baking\, travelling\, know ing new people\, and exploring new activities.\n\nAbstract: Radiotherapy p lays a crucial role in modern medicine but requires considerable time for manually contouring radio-sensitive organs at risk\, which can delay treat ment processing. With the significant success of deep convolutional neural networks\, auto-segmentation in medical image analysis has shown substant ial improvements in saving time and reducing inter-operator variability. W hile convolutional neural networks utilise the locality of convolution ope rations\, they lose global and long-range semantic information. To address this\, we propose a cascaded transformer U-net for medical image segmenta tion that compensates for long-range dependencies and mitigates computatio nal requirements without compromising performance.\n\n\n\n*Machine learnin g for treatment stratification in kidney cancer - Rebecca Wray\, PhD Stude nt\, Early Cancer Institute\, University of Cambridge & Dr Hania Paverd\, Clinical Research Training Fellow\, Early Cancer Institute\, University of Cambridge*\n\nRebecca completed her undergraduate degree in Biosciences f rom Durham University\, where she specialised in Biochemistry and Molecula r Biology\, before moving to Cambridge to join CS Genetics\, a biotechnolo gy start-up investigating novel single-cell RNA-sequencing methods. She th en joined Dr Annie Speak’s group at the Cambridge Institute for Therapeu tic Immunology and Infectious Disease (CITIID). Currently\, Rebecca is in her second year of the prestigious Cancer Research UK (CRUK) Cambridge Cen tre MRes + PhD programme. Under the mentorship of Dr Mireia Crispin-Ortuza r and Dr James Jones\, she is employing data-driven approaches to uncover novel biomarkers and mechanisms related to treatment failure and resistanc e in kidney cancer.\n\nHania 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\, befo re moving onto Specialty Training in Radiology at Addenbrooke’s Hospital . She is currently in her first year of PhD as a Clinical Research Trainin g Fellow at the Early Cancer Institute in Cambridge\, working under the su pervision of Dr Mireia Crispin-Ortuzar and Dr Matthew Hoare. Her PhD resea rch focuses on computational analysis of CT and MRI scans\, integrated wit h other data modalities such as genomic data\, to enhance risk stratificat ion for patients with liver disease and improve early detection of liver c ancer.\n\nAbstract: Clear cell renal cell carcinoma (ccRCC) is the most le thal urological malignancy. The cancer is highly heterogeneous\, and thera py response varies between patients. In a subset of cases\, the tumour ext ends into the renal vein and inferior vena cava\, termed venous tumour thr ombus (VTT)\, which complicates surgical intervention. While response sign atures have been developed for metastatic RCC\, there’s a notable gap fo r patients with VTT. Here we present molecular analysis of data from NAXIV A\, 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 d evelop a machine learning model which uses baseline and dynamic data taken from blood samples early in treatment\, and demonstrates good patient str atification. We report novel biological markers for positive response to a nti-angiogenic agents\, including CCL17\, IL-12p70\, PlGF and Tie-2. This research paves the way for better patient stratification and response pred iction\, offering promising avenues for personalised therapy in ccRCC.\n\n \n\nThis is a hybrid event so you can also join via Zoom:\n\nhttps://zoom. us/j/99050467573?pwd=UE5OdFdTSFdZeUtIcU1DbXpmdlNGZz09\n\nMeeting ID: 990 5 046 7573 and Passcode: 617729\n\n\n\n*We look forward to your participatio n! If you are interested in getting involved and presenting your work\, pl ease email Ines Machado at im549@cam.ac.uk*\n\n\n\nFor more information ab out this seminar series\, see: https://www.integratedcancermedicine.org/re search/cambridge-medai-seminar-series/ LOCATION:Jeffrey Cheah Biomedical Centre (Main Lecture Theatre)\, Universi ty of Cambridge END:VEVENT END:VCALENDAR