232 citations · 573 across the 29 of their papers we have counts for
15 papers · 1 filter
Improving Performance, Robustness, and Fairness of Radiographic AI Models with Finely-Controllable Synthetic Data
Stefania L. Moroianu, Christian Bluethgen, Pierre Chambon +8
Achieving robust performance and fairness across diverse patient populations remains a challenge in developing clinically deployable deep learning models for diagnostic imaging. Sy…
SOE: SO(3)-Equivariant 3D MRI Encoding
Shizhe He, Magdalini Paschali, Jiahong Ouyang +3
Representation learning has become increasingly important, especially as powerful models have shifted towards learning latent representations before fine-tuning for downstream task…
Merlin: A Computed Tomography Vision-Language Foundation Model and Dataset
Louis Blankemeier, Ashwin Kumar, Joseph Paul Cohen +37
The large volume of abdominal computed tomography (CT) scans coupled with the shortage of radiologists have intensified the need for automated medical image analysis tools. Previou…
A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation
Zhihong Chen, Maya Varma, Justin Xu +20
Over 1.4 billion chest X-rays (CXRs) are performed annually due to their cost-effectiveness as an initial diagnostic test. This scale of radiological studies provides a significant…
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
Walter A. Simson, Magdalini Paschali, Vasiliki Sideri-Lampretsa +2
Ultrasound is an adjunct tool to mammography that can quickly and safely aid physicians with diagnosing breast abnormalities. Clinical ultrasound often assumes a constant sound spe…
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Tobias Czempiel, Aidean Sharghi, Magdalini Paschali +2
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis. So far many different…
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni +7
During the first wave of COVID-19, hospitals were overwhelmed with the high number of admitted patients. An accurate prediction of the most likely individual disease progression ca…
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Christoph Berger, Magdalini Paschali, Ben Glocker +1
Image classification models deployed in the real world may receive inputs outside the intended data distribution. For critical applications such as clinical decision making, it is…
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
Tobias Czempiel, Magdalini Paschali, Daniel Ostler +3
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences. A novel attention regularization loss encourages the…
Signal Clustering with Class-independent Segmentation
Stefano Gasperini, Magdalini Paschali, Carsten Hopke +2
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches. In this paper we propose a Deep Learning-based clus…
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
Magdalini Paschali, Muhammad Ferjad Naeem, Walter Simson +3
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing. A Deep Neural Network (DNN), inspired by Bag-of-Features models…
End-to-End Learning-Based Ultrasound Reconstruction
Walter Simson, Rüdiger Göbl, Magdalini Paschali +4
Ultrasound imaging is caught between the quest for the highest image quality, and the necessity for clinical usability. Our contribution is two-fold: First, we propose a novel full…
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Magdalini Paschali, Stefano Gasperini, Abhijit Guha Roy +2
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization meth…
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
Magdalini Paschali, Sailesh Conjeti, Fernando Navarro +1
In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also i…
Learning Robust Hash Codes for Multiple Instance Image Retrieval
Sailesh Conjeti, Magdalini Paschali, Amin Katouzian +1
In this paper, for the first time, we introduce a multiple instance (MI) deep hashing technique for learning discriminative hash codes with weak bag-level supervision suited for la…