BMVA Symposium: Computer Vision in Cancer

Chairs: Greg Slabaugh & Constantino Carlos Reyes-Aldasoro (City, University of London)

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)

Call for Papers:

This BMVA one-day meeting will present state-of-the-art developments in Computer Vision applied to Cancer Data Analysis.

This one-day meeting will be dedicated to technical advances that have the 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.

Programme

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

Poster presenters

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. Nathan Olliverre, City, University of London

Pairwise Mixture Model for Unmixing Partial Volume Effect in Multi-voxel MR Spectroscopy of Brain Tumour Patients

13. Paul Tar, University of Manchester

Mathematical Modelling of Tumour Heterogeneity Increases Statistical Power in Assessing Response to Therapy

14. Arti Taneja, Amity Institute of Information Technology

Breast Cancer Grading Visualisation Based on Earth Mover’s Distan

15. Muhammad Shaban, University of Warwick

Representation-Aggregation Networks for Segmentation of Multi-Gigapixel Histology Images

16. Tzu-Hsi Song, University of Warwick
Simultaneous Cell Detection and Classification with an Asymmetric Deep Autoencoder in Bone Marrow Histology Images
17. Simon Graham, University of Warwick
Classification of Lung Cancer Histology Images using Patch-Level Summary Statistics

18. Talha Qaiser, University of Warwick

Tumor Segmentation in Whole Slide Images using Persistent Homology and Deep Convolutional Features

19. Najah Alsubaie, University of Warwick
Survival Analysis of Lung Cancer Patients using Nuclear Features