Search Results for author:
Found 5 papers, 4 papers with code
NMGrad: Advancing Histopathological Bladder Cancer Grading with Weakly Supervised Deep Learning
To distinguish different levels of malignancy within specific regions of the slide, we include the origins of the tiles in our analysis.
Self-Contrastive Weakly Supervised Learning Framework for Prognostic Prediction Using Whole Slide Images
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction.
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offs
We developed DL pipelines using two MoEs and two multiclass models of state-of-the-art deep convolutional neural networks (DCNNs) and vision transformers (ViTs).
Ranked #1 on
Artifact Detection
on HistoArtifacts
(1:1 Accuracy metric)
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological images
We achieved 0. 996 and 0. 938 F1 scores for blur and folded tissue detection on unseen data, respectively.
Ranked #1 on
Artifact Detection
on HistoArtifacts
Challenging mitosis detection algorithms: Global labels allow centroid localization
Mitotic activity is a crucial proliferation biomarker for the diagnosis and prognosis of different types of cancers.
