Computer Vision and Modelling in Cancer.
MEETING DATE: Wednesday 11 October 2017


9:00-9:35 Arrival and registration
9:35-9:40 Welcome

9:40-10:00 Jola Mirecka, University of Oxford
The Influence of Local Variation and Local Similarities on
Tumour Subregional Analysis

10:00-10:45 Keynote speech: Dr Yinyin Yuan, Institute of Cancer Research
Deciphering the Tumour Ecosystem with Histology Deep Learning

10:45-11:05 Antonia Creswell, Imperial College London
Denoising Adversarial Autoencoders: Classifying Skin Lesions
Using Limited Labelled Training Data

11:05-11:30 Coffee break

11:30-12:15 Keynote speech: Dr Ben Glocker, Imperial College London
Brain Tumour Segmentation with Deep Neural Nets

12:15-14:00 Lunch concurrent with poster presentations (please see below)

14:00-14:45 Keynote speech: Prof Nasir Rajpoot, Warwick University
Computational Pathology Research at Warwick

14:45-15:05 Tim Ingham-Dempster, University of Sheffield
Multi-scale Modelling of Carcinogenic Field Spread in the Human Colon

15:05-15:25 Zhaoyang Xu, Queen Mary University of London
Multi-feature Fusion for Semantic Segmentation of CRLM Border

15:25-15:45 Tea break

15:45-16:30 Keynote speech: Prof Helen Byrne, University of Oxford
Mathematical Modeling: A Valuable Tool for Interpreting Cancer Images?

16:35 Final remarks and conclusion

Untitled title
Poster presentations
1. Guang Yang, Imperial College London
MRI Brain Tumor Segmentation using Random Forests and Fully Convolutional Networks
2. Zobia Akram, Aberystwyth University
Mammographic Mass Classification Using Filter Response Patches
3. Said Pertuz, Tampere University of Technology
Algorithms and Methods for Computerized Analysis of Mammography Images for Breast Cancer Risk Assessment
4. Zheqi Yu, University of Wolverhampton
A Real-time Assistive Diagnosis System for Esophageal Adenocarcinoma and Colorectal Cancer
5. Bartlomiej Papiez, University of Oxford
Towards Automated Non-invasive Monitoring of Metastatic Tumour Growth for Preclinical Studies
6. Liping Wang, Aberystwyth University
Prostate Cancer Detection using Features Extracted from Multi-parametric MRI
7. Adam Szmul, University of Oxford
A Novel Approach for Deformable Lung Image Registration Using Over-Segmentation based on Supervoxels, Graph Cuts and Guided Image Filtering
8. Joseph Jacobs, University College London
Semi-supervised Prostate Nucleus Classification with Convolutional Neural Networks
9. José Alonso Solís-Lemus, City, University of London
Segmentation of Overlapping Macrophages Using Anglegram Analysis
10. Alison Pouplin, Imperial College London
Modelling the Evolution of Skin Lesions Over Time Using a Bidirectional Generative Adversarial Network
11. Nashid Alam, Aberystwyth University
Computer-aided Classification of Microcalcification Cluster in Digitized Mammogram for Early Diagnosis of Breast Cancer
12. Arti Taneja, Amity Institute of Information Technology
13. Nathan Olliverre, City, University of London
Pairwise Mixture Model for Unmixing Partial Volume Effect in Multi-voxel MR Spectroscopy of Brain Tumour Patients
14. Paul Tar, University of Manchester
Mathematical Modelling of Tumour Heterogeneity Increases Statistical Power in Assessing Response to Therapy
15. Muhammad Shaban, Warwick University
Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images
16. Tzu-Hsi Song, Warwick University
Simultaneous Cell Detection and Classification with an Asymmetric Deep Autoencoder in Bone Marrow Histology Images
17. Simon Graham, Warwick University
Classification of Lung Cancer Histology Images using Patch-Level Summary Statistics
18. Talha Qaiser, Warwick University
Tumor Segmentation in Whole Slide Images using Persistent Homology and Deep Convolutional Features
19. Najah Alsubaie, Warwick University
Survival Analysis of Lung Cancer Patients using Nuclear Features
20. Navid Alemi, Warwick University
Deep Learning for Lung Cancer Histology Image Analysis

Computer Vision and Modelling in Cancer: BMVA 1-Day Meeting
This BMVA one-day meeting will present state-of-the-art developments in Computer Vision, Computational Modelling and Mathematical Analysis applied to Cancer.

Recent progress in imaging hardware, acquisition techniques, and algorithmic processing of data have led to advances in detection, diagnosis, staging, treatment, and follow-up in cancer-related clinical workflows, as well as fundamental understanding of cancer modelling and dynamics.  Cancer imaging includes varied modalities, and numerous scales including nano, micro, and macro. 
This one-day meeting will be dedicated to technical advances that have potential for clinical relevance, and seeks to bring together a collection of recently developed approaches in this domain. We hope the methods presented will inspire future research both from theoretical and practical viewpoints to spur further advances in the field. 

The topics include, but are not limited to:
• segmentation in cancer imaging: from cellular structures to cells to lesions to tumours
• tracking of cells in metastasis and migration processes
• multi-modal registration of cancer images
• modelling of cancer cells, tumours or vasculature
• multi-scale cancer modelling
• image-based interventional techniques for cancer treatment
• tissue characterisation from images
• histopathology image analysis

Keynote Speakers
Professor Helen Byrne, University of Oxford

Professor Nasir Rajpoot, Warwick University

Dr Ben Glocker, Imperial College London

Dr Yinyin Yuan, Institute for Cancer Research

IET Computer Vision Special Issue

Selected papers of this one-day meeting will be invited to submit an extended version to be included in a special issue of

IET Computer Vision;jsessionid=3rffg1dr31lfe.x-iet-live-01

Further details of the meeting and other 1-day BMVA meetings, and registration page can be found in the BMVA website:

Author details and abstract
Please complete the following fields. If, in addition to these submision, you would like to add one PDF document please send it to the organisers:

Constantino Carlos Reyes-Aldasoro,
Greg Slabaugh,

City, University of London

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