Published in NeurIPS ML4H, 2017
Knee X-ray exams consisting of sets of images and corresponding radiological reports can be used as part of a learning framework to determine image-text correspondence and hence automate the generation of such reports for new X-ray exams.
Recommended citation: Gasimova, A., Montana, G., Rueckert, D. (2017). "Automated Knee X-ray Report Generation." NeurIPS ML4H.
Published in MICCAI ML-CDS, 2019
Given a small amount of manual annotations, clinically and visually-important concepts can be learned from raw textual radiology reports and consequently used at image annotations in automated report generation.
Recommended citation: Gasimova, A., 2019. "Automated enriched medical concept generation for chest X-ray images." In Interpretability of Machine Intelligence in Medical Image Computing and Multimodal Learning for Clinical Decision Support (pp. 83-92). Springer, Cham.
Published in MICCAI, 2020
Semantic-preserving latent space for minor as well as extremely undersampled MR images capable of achieving promising results on a diagnostic report generation task.
Recommended citation: Gasimova, A., Seegoolam, G., Chen, L., Bentley, P. and Rueckert, D., 2020, October. "Spatial Semantic-Preserving Latent Space Learning for Accelerated DWI Diagnostic Report Generation." In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 333-342). Springer, Cham.
Published in ATS Abstracts, 2023
Patients with incidental pulmonary nodules that require follow-up can be identified and managed using automated NLP.
Recommended citation: Dotson T.L., Gasimova A., Watkins J., Chometon Q., Bellinger C.R., (2023). "Identifying Patients With Pulmonary Nodules From CT Radiology Reports Using Natural Language Processing (NLP)." ATS.
Published in ESMO Abstracts, 2023
ML model outperforms clinical staging prediction of lung cancer recurrence in preoperative settings.
Recommended citation: Valter A., Kordemets T., Gasimova A., Waterfield Price N., Freitag L., Vanakesa T., Almre I., Oselin K. (2023). "Machine learning model for predicting lung cancer recurrence after surgical treatment: A retrospective study using NLST and European hospital data." ESMO.
Published in ESMO Abstracts, 2024
The ML survival model outperforms clinical staging in patient risk-stratification and time-dependent lung cancer recurrence prediction.
Recommended citation: Valter A., Kordemets T., Gasimova A., Heames B., Waterfield Price N., Hodgkinson G., Vanakesa T., Almre I., Freitag L., Carbone D., Oselin K. (2024). "Imaging AI prognosis of early stage lung cancer using CT radiomics." ESMO.
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Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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