M1.7: Publications

Journal Articles
  • Chatterjee S, Saad F, Sarasaen C, Ghosh S, Khatun R, Radeva P, Rose G, Stober S, Speck O, and Nürnberger A (tba): Exploration of interpretability techniques for deep COVID-19 classification using chest X-ray images. Manuscript submitted to Scientific Reports (under review): arXiv:2006.02570 (1-16). DOI: 10.21203/rs.3.rs-1396136/v1; 10.48550/ARXIV.2006.02570. URL: https://www.researchsquare.com/article/rs-1396136/v1
  • Chatterjee S, Sarasaen C, Rose G, Nürnberger A, and Speck O (tba): DDoS-UNet: Incorporating temporal information using dynamic dual-channel UNet for enhancing super-resolution of dynamic MRI. Manuscript submitted to Medical Image Analysis (under review): arXiv:2202.05355 (1-12). DOI: 10.48550/ARXIV.2202.05355. URL: https://arxiv.org/abs/2202.05355
  • Sarasaen C, Chatterjee S, Breitkopf M, Rose G, Nürnberger A, and Speck O (2021): Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge. Artificial Intelligence in Medicine, 121: 102196 (1-11). DOI: 10.1016/j.artmed.2021.102196. URL: https://www.sciencedirect.com/science/article/pii/S0933365721001895
  • Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2022): ReconResNet: Regularised residual learning for MR image reconstruction of Undersampled Cartesian and Radial data. Computers in Biology and Medicine, 143: 105321 (1-17). DOI: 10.1016/j.compbiomed.2022.105321. URL: https://www.sciencedirect.com/science/article/abs/pii/S0010482522001135
  • Chatterjee S, Das A, Mandal C, Mukhopadhyay B, Vipinraj M, Shukla A, Rao R N, Sarasaen C, Speck O, and Nürnberger A (2022): TorchEsegeta: Framework for interpretability and explainability of image-based Deep Learning models. Applied Sciences, 12(4): 1834 (1-20). DOI: 10.3390/app12041834. URL: https://www.mdpi.com/2076-3417/12/4/1834
  • Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2022): DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data. Journal of Imaging, 8(10): 259 (1-22). DOI: 10.3390/jimaging8100259. URL: https://www.mdpi.com/2313-433X/8/10/259

Conference Papers
  • Chatterjee S, Sarasaen C, Rose G, Nürnberger A, and Speck O (2022): DDoS-UNet: Incorporating temporal information using Dynamic Dual-channel UNet for enhancing super-resolution of dynamic MRI. Paper presented at Medical Imaging with Deep Learning (MIDL) 2022, Zürich, Switzerland, Jul 6-8, 2022. p. — (1-3). Oral presentation and poster. DOI: —. URL: https://openreview.net/forum?id=S7S6gPtbKU4
  • Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2021): DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data. Paper presented at Medical Imaging with Deep Learning (MIDL) 2021, Lübeck, Germany, Jul 7-9, 2021. p. — (1-3; ID: 109). Oral presentation (online). DOI: —. URL: https://openreview.net/forum?id=2t0_AxD1otB
  • Chatterjee S, Breitkopf M, Sarasaen C, Yassin H, Rose G, Nürnberger A, and Speck O (2021): ReconResNet: Regularised residual learning for MR image reconstruction of undersampled cartesian and radial data. Paper presented at Medical Imaging with Deep Learning (MIDL) 2021, Lübeck, Germany, Jul 7-9, 2021. p. — (1-3; ID: 110). Oral presentation (online). DOI: —. URL: https://openreview.net/forum?id=KNEKu-W4Avz
  • Sarasaen C, Chatterjee S, Breitkopf M, Iuso D, Rose G, and Speck O (2019): Breathing deformation model —application to multi-resolution abdominal MRI. Paper presented at 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2019), Berlin, Germany, Jul 23-27, 2019. IEEE. p. 2769-72. Oral presentation and poster. DOI: 10.1109/EMBC.2019.8857706. URL: https://ieeexplore.ieee.org/document/8857706

Further Contributions (Abstracts, Talks, Posters)
  • Sarasaen C, Chatterjee S, Nürnberger A, and Speck O (2021): DDoS: Dynamic dual-channel U-Net for improving deep learning based super-resolution of abdominal dynamic MRI. Abstract presented at ESMRMB 2021 Online — 38th Annual Scientific Meeting, Oct 7-9, 2021. p. S44. Oral presentation (online; ID: S06.O3). DOI: 10.1007/s10334-021-00947-8. URL: https://link.springer.com/article/10.1007/s10334-021-00947-8
  • Chatterjee S, Prabhu K, Pattadkal M, Bortsova G, Sarasaen C, Dubost F, Mattern H, De Bruijne M, Speck O, and Nürnberger A (2021): DS6, Deformation-aware Semi-supervised Learning: Application to Small Vessel Segmentation with Noisy Training Data. Abstract presented at Stanford University / Contrastive & SS Learning Group, Stanford, CA, USA, Mar 12, 2021. p. —. Invited talk / oral presentation (online). URL: https://youtu.be/p1RrvlMMhOI
  • Sarasaen C, Chatterjee S, Nürnberger A, and Speck O (2020): Super resolution of dynamic MRI using deep learning, enhanced by prior-knowledge. Abstract presented at ESMRMB 2020 Online - 37th Annual Scientific Meeting, Sep 30-Oct 2, 2020. p. S28-29. Oral presentation (online; ID: S03.04). DOI: 10.1007/s10334-020-00874-0. URL: https://link.springer.com/article/10.1007/s10334-020-00874-0
  • Sarasaen C, Chatterjee S, Breitkopf M, Rose G, and Speck O (2019): Generating breathing deformation model from low resolution 4D MRI. Abstract presented at Recent progress and developments: 4th Conference on Image-Guided Interventions & Digitalization in Medicine (IGIC 2019), Mannheim, Germany, Nov 4-5, 2019. p. —. Poster. URL: http://www.igic.de/upload/IGIC-2019/Programm_engl_20191024.pdf
  • Sarasaen C, Chatterjee S, Breitkopf M, Rose G, and Speck O (2019): Konzeptstudie eines interventionellen Computertomographen. Abstract presented at Recent progress and developments: 4th Conference on Image-Guided Interventions & Digitalization in Medicine (IGIC 2019), Mannheim, Germany, Nov 4-5, 2019. p. —. Poster. URL: http://www.igic.de/deutsch/igic-2019/willkommen-2019/index.html

Last Modification: 11.12.2022 - Contact Person: