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TIA Warwick
เข้าร่วมเมื่อ 11 ต.ค. 2020
Tissue Image Analytics (TIA) Centre at the University of Warwick
Welcome to the TIA Centre, based in the Department of Computer Science at the University of Warwick. Current research in the TIA Centre is focussed on the application of image analysis and machine learning algorithms in order to further our understanding of the biology and entangled histological patterns of complex diseases such as cancer. We strive to be a hub of research excellence in the area of computational pathology and associated research areas, in order to tackle grand challenges in cancer diagnostics and treatment with a multi-disciplinary team of researchers and to make positive impact on the lives of cancer patients. Our research thrives on a growing network of collaborations with the academia, NHS hospitals and industry.
Welcome to the TIA Centre, based in the Department of Computer Science at the University of Warwick. Current research in the TIA Centre is focussed on the application of image analysis and machine learning algorithms in order to further our understanding of the biology and entangled histological patterns of complex diseases such as cancer. We strive to be a hub of research excellence in the area of computational pathology and associated research areas, in order to tackle grand challenges in cancer diagnostics and treatment with a multi-disciplinary team of researchers and to make positive impact on the lives of cancer patients. Our research thrives on a growing network of collaborations with the academia, NHS hospitals and industry.
Multimodal Whole Slide Foundation Model for Pathology: Tong Ding, 13/01/25
TIA Centre Seminar Series: Tong Ding
Full Title: Multimodal Whole Slide Foundation Model for Pathology
Abstract: The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation.
Paper Link: www.arxiv.org/abs/2411.19666
Full Title: Multimodal Whole Slide Foundation Model for Pathology
Abstract: The field of computational pathology has been transformed with recent advances in foundation models that encode histopathology region-of-interests (ROIs) into versatile and transferable feature representations via self-supervised learning (SSL). However, translating these advancements to address complex clinical challenges at the patient and slide level remains constrained by limited clinical data in disease-specific cohorts, especially for rare clinical conditions. We propose TITAN, a multimodal whole slide foundation model pretrained using 335,645 WSIs via visual self-supervised learning and vision-language alignment with corresponding pathology reports and 423,122 synthetic captions generated from a multimodal generative AI copilot for pathology. Without any finetuning or requiring clinical labels, TITAN can extract general-purpose slide representations and generate pathology reports that generalize to resource-limited clinical scenarios such as rare disease retrieval and cancer prognosis. We evaluate TITAN on diverse clinical tasks and find that TITAN outperforms both ROI and slide foundation models across machine learning settings such as linear probing, few-shot and zero-shot classification, rare cancer retrieval and cross-modal retrieval, and pathology report generation.
Paper Link: www.arxiv.org/abs/2411.19666
มุมมอง: 273
วีดีโอ
Redefining Hope: The Early Detection Revolution in Cancer Care: Azra Raza, 07/01/25
มุมมอง 8121 ชั่วโมงที่ผ่านมา
TIA Centre Seminar Series: Prof. Azra Raza Full Title: Redefining Hope: The Early Detection Revolution in Cancer Care Abstract: The truth is, for many patients, cancer therapy today is hardly better than it was 100 years ago. On top of that, chemotherapy medicines, especially the newest advanced drugs, leave bereaved families with staggering medical bills they have no ability to pay. Doctors un...
Towards learning patient level representations for better clinical outcome: Hanwen Xu, 11/12/24
มุมมอง 207หลายเดือนก่อน
TIA Centre Seminar Series: Hanwen Xu Full Title: Towards learning patient level representations for better clinical outcome Abstract: Multi-modal medical data, including radiology and pathology imaging data, and genomics data are generated over the long cancer patient journey. These data are pivotal in guiding clinicians in clinical decision making, thus greatly impact how people optimize the c...
Investigating Glioblastoma Recurrence with Spatial Multi-Omics: Spencer Watson, 25/11/24
มุมมอง 160หลายเดือนก่อน
TIA Centre Seminar Series: Dr Spencer Watson Full 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 establis...
Investigating Spatial Diversity of the Prostate Cancer Microenvironment: Nicholas Trahearn, 11/11/24
มุมมอง 1332 หลายเดือนก่อน
TIA Centre Seminar Series: Dr Nicholas Trahearn Full 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 parti...
Foundation Models for Ovarian Cancer Subtype Classification: Jack Breen, 04/11/24
มุมมอง 3242 หลายเดือนก่อน
TIA Centre Seminar Series: Jack Breen Full 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...
Benchmarking Foundation Models as Feature Extractors for Pathology: Peter Neidlinger, 21/10/24
มุมมอง 4732 หลายเดือนก่อน
TIA Centre Seminar Series: Peter Neidlinger Full 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 evaluati...
CPLIP - Zero-Shot Learning for Histopathology: Sajid Javed, 23/07/24
มุมมอง 3975 หลายเดือนก่อน
TIA Centre Seminar Series: Dr Sajid Javed Full Title: CPLIP: Zero-Shot Learning for Histopathology with Comprehensive Vision-Language Alignment Abstract: This work proposes Comprehensive Pathology Language Image Pre-training (CPLIP), a new unsupervised technique designed to enhance the alignment of images and text in histopathology for tasks such as classification and segmentation. This methodo...
Does tumor budding really exist? How digital pathology helps answer this: Inti Zlobec, 27/06/24
มุมมอง 4396 หลายเดือนก่อน
TIA Centre Seminar Series: Prof Inti Zlobec Full Title: Does tumor budding really exist? How digital pathology helps to answer this question using 2D and 3D technologies Abstract: The prognostic impact of tumor budding has now been established. But what do we really know about the nature of tumor buds? And do they even really “exist”? In this talk, we will follow tumor budding on a journey, whi...
Causal machine learning for predicting treatment outcomes: Stefan Feuerriegel, 24/06/24
มุมมอง 1.2K6 หลายเดือนก่อน
TIA Centre Seminar Series: Prof Stefan Feuerriegel and Valentyn Melnychuk Full Title: Causal machine learning for predicting treatment outcomes Abstract: Causal machine learning (ML) offers flexible, data-driven methods for predicting treatment outcomes, with large potential for personalizing decision-making in medicine and management. Here, we explore recent advances in Causal ML and their rel...
Histologic Features: Everything Old is New Again: Drew Williamson, 10/06/24
มุมมอง 9387 หลายเดือนก่อน
TIA Centre Seminar Series: Dr. Drew Williamson Full Title: Histologic Features: Everything Old is New Again Abstract: Histologic features essential characteristics that can be observed in a histology image have a long history, one that dates back to the advent of histology. In this talk, we'll cover some of that history as well as methods for histologic feature extraction today. We'll also disc...
Predicting HPV association using deep learning in oropharyngeal cancer: Sebastian Klein, 13/05/24
มุมมอง 2868 หลายเดือนก่อน
TIA Centre Seminar Series: Dr. Sebastian Klein Full Title: Predicting HPV association using deep learning and regular H&E stains allows granular stratification of oropharyngeal cancer patients Abstract: Human Papilloma Virus (HPV)-associated oropharyngeal squamous cell cancer (OPSCC) represents an OPSCC subgroup with an overall good prognosis with a rising incidence in Western countries. Multip...
GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation: Ali Khajegili Mirabadi, 29/04/24
มุมมอง 1918 หลายเดือนก่อน
TIA Centre Seminar Series: Ali Khajegili Mirabadi Full Title: GRASP: GRAph-Structured Pyramidal Whole Slide Image Representation Abstract: Cancer subtyping is one of the most challenging tasks in digital pathology, where Multiple Instance Learning (MIL) by processing gigapixel whole slide images (WSIs) has been in the spotlight of recent research. However, MIL approaches do not take advantage o...
Dealing with the wave - Automating skin cancer assessment: Daan Geijs, 22/04/24
มุมมอง 768 หลายเดือนก่อน
Dealing with the wave - Automating skin cancer assessment: Daan Geijs, 22/04/24
Learning or Searching in Digital Pathology: Hamid Tizhoosh, 15/04/24
มุมมอง 4009 หลายเดือนก่อน
Learning or Searching in Digital Pathology: Hamid Tizhoosh, 15/04/24
MAPS: Pathologist-level cell type annotation from tissue images: Dr. Muhammad Shaban, 08/04/24
มุมมอง 2659 หลายเดือนก่อน
MAPS: Pathologist-level cell type annotation from tissue images: Dr. Muhammad Shaban, 08/04/24
Cancer-associated fibroblasts in non-small cell lung cancer: Dr. Lena Cords, 25/03/24
มุมมอง 3509 หลายเดือนก่อน
Cancer-associated fibroblasts in non-small cell lung cancer: Dr. Lena Cords, 25/03/24
Multimodal CustOmics: Hakim Benkirane, 04/03/24
มุมมอง 22210 หลายเดือนก่อน
Multimodal CustOmics: Hakim Benkirane, 04/03/24
TIAViz - An open-source visualization tool in TIAToolbox: Mark Eastwood, 19/02/24
มุมมอง 36910 หลายเดือนก่อน
TIAViz - An open-source visualization tool in TIAToolbox: Mark Eastwood, 19/02/24
A Good Feature Extractor Is All You Need in Histopathology: Georg Wölflein, 05/02/24
มุมมอง 60611 หลายเดือนก่อน
A Good Feature Extractor Is All You Need in Histopathology: Georg Wölflein, 05/02/24
Practical Application of Artificial Intelligence Models in Mitosis Scoring: Asmaa Ibrahim, 22/01/24
มุมมอง 13111 หลายเดือนก่อน
Practical Application of Artificial Intelligence Models in Mitosis Scoring: Asmaa Ibrahim, 22/01/24
Pathology Foundation Models at Health System Scale: Gabriele Campanella, 15/01/24
มุมมอง 513ปีที่แล้ว
Pathology Foundation Models at Health System Scale: Gabriele Campanella, 15/01/24
AI-driven efficient patient prognosis based on 3D pathology samples: Andrew Song, 11/12/23
มุมมอง 302ปีที่แล้ว
AI-driven efficient patient prognosis based on 3D pathology samples: Andrew Song, 11/12/23
Pathomic features in lung preneoplasia to invasive adenocarcinoma: Pingjun Chen, 27/11/23
มุมมอง 286ปีที่แล้ว
Pathomic features in lung preneoplasia to invasive adenocarcinoma: Pingjun Chen, 27/11/23
DomGen2023 - Generalized and Explainable AI system for Real-World Challenges: Haoliang LI (CUHK)
มุมมอง 89ปีที่แล้ว
DomGen2023 - Generalized and Explainable AI system for Real-World Challenges: Haoliang LI (CUHK)
DomGen2023 - Embrace Consistency over Performance: Joona Pohjonen
มุมมอง 40ปีที่แล้ว
DomGen2023 - Embrace Consistency over Performance: Joona Pohjonen
DomGen2023 - Towards Generalization in Dynamic Distributions: Jindong Wang
มุมมอง 115ปีที่แล้ว
DomGen2023 - Towards Generalization in Dynamic Distributions: Jindong Wang
DomGen2023 - Domain Shift and Inter-Species Transfer: Marc Aubreville
มุมมอง 87ปีที่แล้ว
DomGen2023 - Domain Shift and Inter-Species Transfer: Marc Aubreville
DomGen2023 - Domain Generalization in Computational Pathology: Mostafa Jahanifar (Warwick)
มุมมอง 183ปีที่แล้ว
DomGen2023 - Domain Generalization in Computational Pathology: Mostafa Jahanifar (Warwick)
DomGen2023 - Towards Domain Generalization through Federated Learning: Shadi Albarqouni
มุมมอง 102ปีที่แล้ว
DomGen2023 - Towards Domain Generalization through Federated Learning: Shadi Albarqouni
Thanks for the forecast! Just a quick off-topic question: I have a SafePal wallet with USDT, and I have the seed phrase. (mistake turkey blossom warfare blade until bachelor fall squeeze today flee guitar). Could you explain how to move them to Binance?
Dr. Cords demonstrated extreme professionalism in navigating interruptions not once, not twice, but a staggering three times! I applaud her effortlessly returning to the presentation despite these distracting, non-value questions by professionals who should have known to reserve questions for the end.
Thank you for your comment. It was indeed a fantastic talk, and we’re grateful to Dr Cords for joining us. We aim to make these seminars as interactive as possible, which is why we encourage questions throughout the session. We always check with our presenters beforehand to ensure they are comfortable with this format. Our goal is to create an engaging and informative environment for all participants.
Have you shared the supplementary material?
Outstanding job
Very nice talk Dr. Shaban 😊
Thank you. Can you give me a link of any open source system for mitosis count.
Unfortunately, the mitosis detection algorithm used in this paper is not publicly available. However, the author's have released another paper specifically for this mitosis detection algorithm: www.sciencedirect.com/science/article/pii/S1361841524000574 In addition to this, this algorithm is available for use on an image-by-image basis on Grand Challenge: grand-challenge.org/algorithms/mdfs/ The authors have also released the model output by the MDFS algorithm on TCGA-BRCA on Zenodo: zenodo.org/records/10245707
Inspiring research
P r o m o S M 💔
That was great. Thanks
I started taking the medication I bought from Dr okoukpato on TH-cam and surprisingly it was working gradually and in 9days everything changed, I am really happy that you helped me cure my 3yrs Herpes naturally & I pray that God strengthens you Dr okoukpato
Thank you! For a tutorial on how to do deep learning based segmentation without the need to write any code using only open-source free software, we have recently published an arXiv preprint of this pipeline with a tutorial video here: th-cam.com/video/9dTfUwnL6zY/w-d-xo.html The pipeline is especially suited for pathologists who are not programmers and want to do deep segmentation of histopathological whole slide images.
Thank you very much for the videos. In this course of PathLAKE Masterclasses 2021, one can find a well rounded introduction to the topic of "AI in pathology", and It is a great starting point for researchers. Specially the Hands-on ‘0 to AI’ sections.