1.3k citations · 1.4k across the 4 of their papers we have counts for
6 papers
Progressive Growing of Patch Size: Curriculum Learning for Accelerated and Improved Medical Image Segmentation
Stefan M. Fischer, Johannes Kiechle, Laura Daza +6
In this work, we introduce Progressive Growing of Patch Size, an automatic curriculum learning approach for 3D medical image segmentation. Our approach progressively increases the…
Video Swin Transformers for Egocentric Video Understanding @ Ego4D Challenges 2022
Maria Escobar, Laura Daza, Cristina González +2
We implemented Video Swin Transformer as a base architecture for the tasks of Point-of-No-Return temporal localization and Object State Change Classification. Our method achieved c…
QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results
Raghav Mehta, Angelos Filos, Ujjwal Baid +89
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges.…
Towards Robust General Medical Image Segmentation
Laura Daza, Juan C. Pérez, Pablo Arbeláez
The reliability of Deep Learning systems depends on their accuracy but also on their robustness against adversarial perturbations to the input data. Several attacks and defenses ha…
The Medical Segmentation Decathlon
Michela Antonelli, Annika Reinke, Spyridon Bakas +55
International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely inv…
SIMBA: Specific Identity Markers for Bone Age Assessment
Cristina González, María Escobar, Laura Daza +3
Bone Age Assessment (BAA) is a task performed by radiologists to diagnose abnormal growth in a child. In manual approaches, radiologists take into account different identity marker…