Jose Dolz

Assistant Professor

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Canada

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Jose Dolz is an Assistant Professor at Ecole de Technologie Superieure de Montreal, Canada. His research focuses on advanced methodologies in medical image analysis, particularly in segmentation and classification, as reflected in his recent work on volumetric organ segmentation and histology images. He has contributed to developing foundation models and exploring the implications of uncertainty in semi-supervised learning.

Jose Dolz

Research Interests

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Articles (15)

Towards Foundation Models and Few-Shot Parameter-Efficient Fine-Tuning for Volumetric Organ Segmentation

GeoLS: an Intensity-based, Geodesic Soft Labeling for Image Segmentation

A Foundation Language-Image Model of the Retina (FLAIR): encoding expert knowledge in text supervision

Anatomically-aware uncertainty for semi-supervised image segmentation

Do we really need dice? The hidden region-size biases of segmentation losses

Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey

Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty

Attention-Based Dynamic Subspace Learners for Medical Image Analysis

Self-Learning for Weakly Supervised Gleason Grading of Local Patterns

Constrained Domain Adaptation for Image Segmentation

MRI and CT bladder segmentation from classical to deep learning based approaches: Current limitations and lessons

Privacy-Net: An Adversarial Approach for Identity-Obfuscated Segmentation of Medical Images

Multi-Scale Self-Guided Attention for Medical Image Segmentation

Discretely-constrained deep network for weakly supervised segmentation

Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

Collaborators (2)

Hoel Kervadec

Eric Granger

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