| Image-based survival prediction for lung cancer patients using CNNS C Haarburger, P Weitz, O Rippel, D Merhof 2019 IEEE 16th international symposium on biomedical imaging (ISBI 2019 …, 2019 | 61 | 2019 |
| Predicting molecular phenotypes from histopathology images: a transcriptome-wide expression–morphology analysis in breast cancer Y Wang, K Kartasalo, P Weitz, B Acs, M Valkonen, C Larsson, ... Cancer research 81 (19), 5115-5126, 2021 | 52 | 2021 |
| Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression-based convolutional neural networks P Weitz, Y Wang, K Kartasalo, L Egevad, J Lindberg, H Grönberg, ... Bioinformatics 38 (13), 3462-3469, 2022 | 18 | 2022 |
| Using deep learning to detect patients at risk for prostate cancer despite benign biopsies B Liu, Y Wang, P Weitz, J Lindberg, J Hartman, W Wang, L Egevad, ... Iscience 25 (7), 2022 | 11 | 2022 |
| The ACROBAT 2022 challenge: automatic registration of breast cancer tissue P Weitz, M Valkonen, L Solorzano, C Carr, K Kartasalo, C Boissin, ... Medical Image Analysis, 103257, 2024 | 9 | 2024 |
| SensInDenT—noncontact sensors integrated into dental treatment units D Teichmann, M Teichmann, P Weitz, S Wolfart, S Leonhardt, M Walter IEEE Transactions on Biomedical Circuits and Systems 11 (1), 225-233, 2016 | 9 | 2016 |
| ACROBAT--a multi-stain breast cancer histological whole-slide-image data set from routine diagnostics for computational pathology P Weitz, M Valkonen, L Solorzano, C Carr, K Kartasalo, C Boissin, ... arXiv preprint arXiv:2211.13621, 2022 | 8 | 2022 |
| An investigation of attention mechanisms in histopathology whole-slide-image analysis for regression objectives P Weitz, Y Wang, J Hartman, M Rantalainen Proceedings of the IEEE/CVF International Conference on Computer Vision, 611-619, 2021 | 8 | 2021 |
| Radiomic feature stability analysis based on probabilistic segmentations C Haarburger, J Schock, D Truhn, P Weitz, G Mueller-Franzes, ... 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), 1188-1192, 2020 | 8 | 2020 |
| ACROBAT-automatic registration of breast cancer tissue P Weitz, M Valkonen, L Solorzano, J Hartman, P Ruusuvuori, ... 10th Internatioal Workshop on Biomedical Image Registration, 2022 | 5 | 2022 |
| Development and prognostic validation of a three-level NHG-like deep learning-based model for histological grading of breast cancer A Sharma, P Weitz, Y Wang, B Liu, J Vallon-Christersson, J Hartman, ... Breast Cancer Research 26 (1), 17, 2024 | 3 | 2024 |
| A Multi-Stain Breast Cancer Histological Whole-Slide-Image Data Set from Routine Diagnostics P Weitz, M Valkonen, L Solorzano, C Carr, K Kartasalo, C Boissin, ... Scientific Data 10 (1), 562, 2023 | 2 | 2023 |
| Artificial intelligence in histopathology image analysis for cancer precision medicine P Weitz Inst för medicinsk epidemiologi och biostatistik/Dept of Medical …, 2023 | 1 | 2023 |
| Increasing the usefulness of already existing annotations through WSI registration P Weitz, V Sartor, B Acs, S Robertson, D Budelmann, J Hartman, ... arXiv preprint arXiv:2303.06727, 2023 | 1 | 2023 |
| A Deep CNN Approach For Predicting Cumulative Incidence Based On Pseudo-Observations PG Ginestet, P Weitz, M Rantalainen, EE Gabriel | 1 | 2021 |
| Prediction of Ki67 scores from H&E stained breast cancer sections using convolutional neural networks P Weitz, B Acs, J Hartman, M Rantalainen Medical Imaging with Deep Learning, 2021 | 1 | 2021 |
| Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images C Boissin, Y Wang, A Sharma, P Weitz, E Karlsson, S Robertson, ... Breast Cancer Research 26 (1), 90, 2024 | | 2024 |
| Validation of spatial gene expression patterns predicted by deep convolutional neural networks from breast cancer histopathology images K Ton, Y Wang, L Pan, K Kartasalo, B Acs, P Weitz, L Zhang, Y Liang, ... Cancer Research 83 (7_Supplement), 5432-5432, 2023 | | 2023 |
| Transcriptome-wide prediction of prostate can-cer gene expression from histopathology im-ages using co-expression based convolutional neural networks P Weitz, Y Wang, K Kartasalo, L Egevad, J Lindberg, H Grönberg, ... | | 2022 |
Mattias RantalainenAssociate professor, Karolinska Institutet, Sweden在 ki.se 的电子邮件经过验证
