Deep Blur Multi-Model (DeepBlurMM) -- a strategy to mitigate the impact of image blur on deep learning model performance in histopathology image analysis

no code implementations • 15 May 2024 • Yujie Xiang, Bojing Liu, Mattias Rantalainen

We propose a multi-model approach, i. e. DeepBlurMM, to alleviate the impact of unsharp image areas and improve the model performance.

WEEP: A method for spatial interpretation of weakly supervised CNN models in computational pathology

1 code implementation • 22 Mar 2024 • Abhinav Sharma, Bojing Liu, Mattias Rantalainen

Deep learning enables the modelling of high-resolution histopathology whole-slide images (WSI).

Binary Classification Weakly-supervised Learning +1

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides

1 code implementation • 4 May 2022 • Adalberto Claudio Quiros, Nicolas Coudray, Anna Yeaton, Xinyu Yang, Bojing Liu, Hortense Le, Luis Chiriboga, Afreen Karimkhan, Navneet Narula, David A. Moore, Christopher Y. Park, Harvey Pass, Andre L. Moreira, John Le Quesne, Aristotelis Tsirigos, Ke Yuan

Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.

Community Detection Representation Learning +2

Using deep learning to detect patients at risk for prostate cancer despite benign biopsies

no code implementations • 27 Jun 2021 • Bojing Liu, Yinxi Wang, Philippe Weitz, Johan Lindberg, Johan Hartman, Lars Egevad, Henrik Grönberg, Martin Eklund, Mattias Rantalainen

As a proof-of-principle, we developed and validated a deep convolutional neural network model to distinguish between morphological patterns in benign prostate biopsy whole slide images from men with and without established cancer.

Hyperparameter Optimization Model Optimization +3