61 citations · 176 across the 56 of their papers we have counts for
9 papers · 1 filter
REFLECT: Rectified Flows for Efficient Brain Anomaly Correction Transport
Farzad Beizaee, Sina Hajimiri, Ismail Ben Ayed +3
Unsupervised anomaly detection (UAD) in brain imaging is crucial for identifying pathologies without the need for labeled data. However, accurately localizing anomalies remains cha…
Leveraging Uncertainty for Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images
Soufiane Belharbi, Jérôme Rony, Jose Dolz +3
Trained using only image class label, deep weakly supervised methods allow image classification and ROI segmentation for interpretability. Despite their success on natural images,…
Looking at the whole picture: constrained unsupervised anomaly segmentation
Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz
Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regio…
Orthogonal Ensemble Networks for Biomedical Image Segmentation
Agostina J. Larrazabal, César Martínez, Jose Dolz +1
Despite the astonishing performance of deep-learning based approaches for visual tasks such as semantic segmentation, they are known to produce miscalibrated predictions, which cou…
Self-learning for weakly supervised Gleason grading of local patterns
Julio Silva-Rodríguez, Adrián Colomer, Jose Dolz +1
Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manu…
Privacy Preserving for Medical Image Analysis via Non-Linear Deformation Proxy
Bach Ngoc Kim, Jose Dolz, Christian Desrosiers +1
We propose a client-server system which allows for the analysis of multi-centric medical images while preserving patient identity. In our approach, the client protects the patient…