Search Results for author: Ramona Woitek

Found 5 papers, 1 papers with code

Improving Generalization Capability of Deep Learning-Based Nuclei Instance Segmentation by Non-deterministic Train Time and Deterministic Test Time Stain Normalization

no code implementations12 Sep 2023 Amirreza Mahbod, Georg Dorffner, Isabella Ellinger, Ramona Woitek, Sepideh Hatamikia

With the advent of digital pathology and microscopic systems that can scan and save whole slide histological images automatically, there is a growing trend to use computerized methods to analyze acquired images.

Instance Segmentation Segmentation +1

Breast MRI radiomics and machine learning radiomics-based predictions of response to neoadjuvant chemotherapy -- how are they affected by variations in tumour delineation?

no code implementations3 Sep 2023 Sepideh Hatamikia, Geevarghese George, Florian Schwarzhans, Amirreza Mahbod, Ramona Woitek

For smoothing and erosion, VOIs yielded the highest number of robust features and the best prediction performance, while ellipse fitting and dilation lead to the lowest robustness and prediction performance for both breast cancer subtypes.

feature selection

Calibrating Ensembles for Scalable Uncertainty Quantification in Deep Learning-based Medical Segmentation

no code implementations20 Sep 2022 Thomas Buddenkotte, Lorena Escudero Sanchez, Mireia Crispin-Ortuzar, Ramona Woitek, Cathal McCague, James D. Brenton, Ozan Öktem, Evis Sala, Leonardo Rundo

On the contrary, we propose a scalable and intuitive framework to calibrate ensembles of deep learning models to produce uncertainty quantification measurements that approximate the classification probability.

Active Learning Uncertainty Quantification

3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning

no code implementations15 May 2020 Maureen van Eijnatten, Leonardo Rundo, K. Joost Batenburg, Felix Lucka, Emma Beddowes, Carlos Caldas, Ferdia A. Gallagher, Evis Sala, Carola-Bibiane Schönlieb, Ramona Woitek

This study showed the feasibility of deep learning based deformable registration of longitudinal abdominopelvic CT images via a novel incremental training strategy based on simulated deformations.

Image Registration

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