| Anomaly detection-inspired few-shot medical image segmentation through self-supervision with supervoxels S Hansen, S Gautam, R Jenssen, M Kampffmeyer Medical Image Analysis 78, 102385, 2022 | 55 | 2022 |
| Considerations for a PAP smear image analysis system with CNN features S Gautam, N Jith, AK Sao, A Bhavsar, A Natarajan arXiv preprint arXiv:1806.09025, 2018 | 39 | 2018 |
| CNN based segmentation of nuclei in PAP-smear images with selective pre-processing S Gautam, A Bhavsar, AK Sao, KK Harinarayan Medical Imaging 2018: Digital Pathology 10581, 246-254, 2018 | 37 | 2018 |
| This looks more like that: Enhancing self-explaining models by prototypical relevance propagation S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer Pattern Recognition 136, 109172, 2023 | 35 | 2023 |
| DeepCerv: Deep neural network for segmentation free robust cervical cell classification OU Nirmal Jith, KK Harinarayanan, S Gautam, A Bhavsar, AK Sao Computational Pathology and Ophthalmic Medical Image Analysis: First …, 2018 | 29 | 2018 |
| Protovae: A trustworthy self-explainable prototypical variational model S Gautam, A Boubekki, S Hansen, S Salahuddin, R Jenssen, M Höhne, ... Advances in Neural Information Processing Systems 35, 17940-17952, 2022 | 21 | 2022 |
| Unsupervised segmentation of cervical cell nuclei via adaptive clustering S Gautam, K Gupta, A Bhavsar, AK Sao Medical Image Understanding and Analysis: 21st Annual Conference, MIUA 2017 …, 2017 | 9 | 2017 |
| Demonstrating the risk of imbalanced datasets in chest x-ray image-based diagnostics by prototypical relevance propagation S Gautam, MMC Höhne, S Hansen, R Jenssen, M Kampffmeyer 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI), 1-5, 2022 | 8 | 2022 |
| ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement S Hansen, S Gautam, SA Salahuddin, M Kampffmeyer, R Jenssen Medical Image Analysis 89, 102870, 2023 | 5 | 2023 |
| HKk,“ S Gautam, A Bhavsar, AK Sao CNN based segmentation of nuclei in PAP-smear images with selective …, 2018 | 5 | 2018 |
| Investigating the Fairness of Large Language Models for Predictions on Tabular Data Y Liu, S Gautam, J Ma, H Lakkaraju arXiv preprint arXiv:2310.14607, 2023 | 3 | 2023 |
| MicroGAN: size-invariant learning of GAN for super-resolution of microscopic images S Gautam, DK Pradhan, PC Chhipa, S Nakajima MICCAI 2019 Computational Pathology Workshop COMPAY, 2019 | 2 | 2019 |
| A self-guided anomaly detection-inspired few-shot segmentation network SA Salahuddin, S Hansen, S Gautam, M Kampffmeyer, R Jenssen CEUR Workshop Proceedings, 2022 | 1 | 2022 |
| Towards Interpretable, Trustworthy and Reliable AI S Gautam UiT Norges arktiske universitet, 2024 | | 2024 |
| Prototypical Self-Explainable Models Without Re-training S Gautam, A Boubekki, M Höhne, MC Kampffmeyer arXiv preprint arXiv:2312.07822, 2023 | | 2023 |
| A self-guided anomaly detection-inspired few-shot segmentation network R Jenssen, S Hansen, S Gautam, M Kampffmeyer | | 2022 |
| Segmentation and Classification of Nuclei in PAP-smear Images for Automated Cervical Cancer Screening S Gautam | | 2017 |
| NUCLEUS-BASED CERVICAL CELL CLASSIFICATION IN PAP SMEAR IMAGES USING DECISION-TREE BASED APPROACH K Gupta, S Gautam, AK Sao, A Bhavsar | | |
Michael Christian KampffmeyerAssociate Professor in Machine Learning, UiT the Arctic University of Norway, TromsøDirección de correo verificada de post.uit.no
