Congratulations to Biraja Ghoshal

Biraja successfully defended his thesis, titled “When the Machine Does Now Know Measuring Uncertainty in Deep Learning Models of Medical Image” on 9th September.

IDA 2022

Toyah Overton presented and Juliana Branescu discussed her poster at the first in-person conference for a while in Rennes, France.

New Papers

Congratulations to those with new publications!:

Toyah Overton, Allan Tucker, Tim James and Dimitar Hristozov. dunXai: DO-U-Net for Explainable (Multi-Label) Image Classification, Intelligent Data Analysis Symposium 2022;

Ghoshal, B., Hikmet, F., Pineau, C., Tucker, A. and Lindskog, C. (2021) ‘DeepHistoClass: A novel strategy for confident classification of immunohistochemistry images using Deep Learning‘. Molecular & Cellular Proteomics, 0 (In Press). pp. 1 – 71. ISSN: 1535-9476

Barnaby E. Walker, Allan Tucker and Nicky Nicolson, Harnessing Large-Scale Herbarium Image Datasets Through Representation Learning, Front. Plant Sci., 13 January 2022 |

New Publications

Congratulations to the following for their recent paper acceptance!

Biraja Ghoshal and Allan Tucker, “On Cost-Sensitive Calibrated Uncertainty in Deep Learning: An application on COVID-19 detection” to IEEE CBMS 2021

Seyed Erfan Sajjadi and Allan Tucker, “Exploiting Clinical Staging Data to Constrain Pseudo-Time Modelling of Disease Progression” to IEEE CBMS 2021

Ben Evans and Allan Tucker
Evans B.C., Tucker A., Wearn O.R., Carbone C. (2021). Reasoning About Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications. In: Koprinska I. et al. (eds) ECML PKDD 2020 Workshops. ECML PKDD 2020. Communications in Computer and Information Science, vol 1323. Springer, Cham.

Lianghao Han
Zhihao Dai, Zhong Li and Lianghao Han. (2021). BoneBert: A BERT-based Automated Information Extraction System of Radiology Reports for Bone Fracture Detection and Diagnosis, IDA 2021.

Yue Shi, Liangxiu Han, Wenjiang Huang, Sheng Chang, Yingying Dong, Darren Dancey, Lianghao Han. (2021). A Biologically Interpretable Two-stage Deep Neural Network (BIT-DNN) For Vegetation Recognition From Hyperspectral Imagery, 10.1109/TGRS.2021.3058782, IEEE Transactions on Geoscience and Remote Sensing, IDA 2021.

Biraja Ghoshal and Allan Tucker
Ghoshal, B. and Tucker, A. (2021) Hyperspherical Weight Uncertainty in Neural Networks, IDA 2021.

Bjaveet Nagaria, Ben Evans, Ashley Mann and Mahir Arzoky (2021). ‘Using an Instant Visual and Text Based Feedback_Tool to Teach Path Finding Algorithms A Concept’.SEENG 2021 Third International Workshop on Software Engineering Education for the Next Generation. Virtual.

Biraja Ghoshal, Bhargab Ghoshal, Stephen Swift, Allan Tucker (2021). “Uncertainty Estimation in SARS-CoV-2 B-cell Epitope Prediction for Vaccine Development”, AIME 2021

Biraja Ghoshal, Stephen Swift, Allan Tucker (2021). Bayesian Deep Active Learning for Medical Image Analysis, AIME 2021

Health Data Research UK

The IDA- Group gave a talk at the inaugaural workshop by the HDR UK Synthetic Data Special Interest Group:

Congatulations to Yani Xue

Congratulations Yani Xue for successfully defending here thesis titled “Effective and Efficient Evolutionary Many-Objective Optimization”

Yani Xue

Congratulations to Lilly Yousefi

Congratulations to Lilly Yousefi for passing her PhD viva with minor corrections.

Leila Yousefi

Thesis Title: “Opening Artificial intelligence Black Box Models: Disease Prediction and Patient Personalisation Using Hidden Variable Discovery and Dynamic Bayesian Networks

Lilly is now working at Brunel Biosciences in collaboration with the Turing Institute and UCL.

New publications

Congratulations to Erfan Sajjadi and Ben Evans for their succesful submissions to the SOGOOD workship at ECML this year:

Erfan Sajjadi et al. Building Trajectories over Topology with TDA-PTS: An Application in Modelling Temporal Phenotypes of Disease

Ben Evans et al. Reasoning about Neural Network Activations: An Application in Spatial Animal Behaviour from Camera Trap Classifications

Other new publications from the group include:

Biraja Ghoshal & Allan Tucker, On Calibrated Model Uncertainty in Deep Learning, ECML Workshop on Uncertainty in Machine Learning

Juan de Benediti et al. Practical Lessons from Generating Synthetic Healthcare Data with Bayesian Networks, SOGOOD, ECML 2020

Arianna Dagliati et al. Using Topological Data Analysis and Pseudo Time Series to Infer Temporal Phenotypes from Electronic Health Records, AI in Medicine Journal