| Interpretable deep learning systems for multi-class segmentation and classification of non-melanoma skin cancer SM Thomas, JG Lefevre, G Baxter, NA Hamilton Medical Image Analysis 68, 101915, 2021 | 124 | 2021 |
| Non-melanoma skin cancer segmentation for histopathology dataset SM Thomas, JG Lefevre, G Baxter, NA Hamilton Data in brief 39, 107587, 2021 | 17 | 2021 |
| Characterization of tissue types in basal cell carcinoma images via generative modeling and concept vectors SM Thomas, JG Lefevre, G Baxter, NA Hamilton Computerized Medical Imaging and Graphics 94, 101998, 2021 | 6 | 2021 |
| Representation Learning for Non-Melanoma Skin Cancer using a Latent Autoencoder SM Thomas arXiv preprint arXiv:2209.01779, 2022 | 1 | 2022 |
| Pathologist Versus Artificial Pathologist: What Do We Really Want (Need) From Machine Learning S Thomas | 1 | 2020 |
| Towards Highly Expressive Machine Learning Models of Non-Melanoma Skin Cancer SM Thomas, JG Lefevre, G Baxter, NA Hamilton arXiv preprint arXiv:2207.05749, 2022 | | 2022 |
| Deep learning methods for the characterisation of non-melanoma skin cancer S Thomas University of Queensland, 2022 | | 2022 |
| Histopathology Non-Melanoma Skin Cancer Segmentation Dataset S Thomas, N Hamilton The University of Queensland, 2021 | | 2021 |
Nicholas A HamiltonThe Institute for Molecular Bioscience, The University of Queensland在 imb.uq.edu.au 的电子邮件经过验证
Nicholas A HamiltonThe Institute for Molecular Bioscience, The University of Queensland在 imb.uq.edu.au 的电子邮件经过验证