282 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Karolinska InstitutetSE28 papers
- KTH Royal Institute of TechnologySE10 papers
- Stockholm South General HospitalSE4 papers
- University of BoråsSE4 papers
- King's College LondonGB3 papers
- Max Planck Institute for Developmental BiologyDE3 papers
- New York UniversityUS3 papers
- Stanford UniversityUS3 papers
- Uppsala UniversitySE3 papers
- Corporació Sanitària Parc TaulíES2 papers
- Gdańsk Medical UniversityPL2 papers
- Hospital Universitari Germans Trias i PujolES2 papers
32 papers
From Black Box to Glass Box: Cross-Model ASR Disagreement to Prioto Review in Ambient AI Scribe Documentation
Abdolamir Karbalaie, Fernando Seoane, Farhad Abtahi
Ambient AI "scribe" systems promise to reduce clinical documentation burden, but automatic speech recognition (ASR) errors can remain unnoticed without careful review, and high-qua…
Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis
Farhad Abtahi, Fernando Seoane, Iván Pau +1
Healthcare AI systems face major vulnerabilities to data poisoning that current defenses and regulations cannot adequately address. We analyzed eight attack scenarios in four categ…
Leveraging Imperfection with MEDLEY A Multi-Model Approach Harnessing Bias in Medical AI
Farhad Abtahi, Mehdi Astaraki, Fernando Seoane
Bias in medical artificial intelligence is conventionally viewed as a defect requiring elimination. However, human reasoning inherently incorporates biases shaped by education, cul…
HealthProcessAI: A Technical Framework and Proof-of-Concept for LLM-Enhanced Healthcare Process Mining
Eduardo Illueca-Fernandez, Kaile Chen, Fernando Seoane +1
Process mining has emerged as a powerful analytical technique for understanding complex healthcare workflows. However, its application faces significant barriers, including technic…
Potential and challenges of generative adversarial networks for super-resolution in 4D Flow MRI
Oliver Welin Odeback, Arivazhagan Geetha Balasubramanian, Jonas Schollenberger +9
4D Flow Magnetic Resonance Imaging (4D Flow MRI) enables non-invasive quantification of blood flow and hemodynamic parameters. However, its clinical application is limited by low s…
Synthesizing Individualized Aging Brains in Health and Disease with Generative Models and Parallel Transport
Jingru Fu, Yuqi Zheng, Neel Dey +2
Simulating prospective magnetic resonance imaging (MRI) scans from a given individual brain image is challenging, as it requires accounting for canonical changes in aging and/or di…
Pediatric brain tumor classification using digital histopathology and deep learning: evaluation of SOTA methods on a multi-center Swedish cohort
Iulian Emil Tampu, Per Nyman, Christoforos Spyretos +7
Brain tumors are the most common solid tumors in children and young adults, but the scarcity of large histopathology datasets has limited the application of computational pathology…
A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations
Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel +30
Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a…
A graph neural network-based model with Out-of-Distribution Robustness for enhancing Antiretroviral Therapy Outcome Prediction for HIV-1
Giulia Di Teodoro, Federico Siciliano, Valerio Guarrasi +6
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This sc…
Deep Learning for Time Series Classification of Parkinson's Disease Eye Tracking Data
Gonzalo Uribarri, Simon Ekman von Huth, Josefine Waldthaler +2
Eye-tracking is an accessible and non-invasive technology that provides information about a subject's motor and cognitive abilities. As such, it has proven to be a valuable resourc…
Incorporating temporal dynamics of mutations to enhance the prediction capability of antiretroviral therapy's outcome for HIV-1
Giulia Di Teodoro, Martin Pirkl, Francesca Incardona +6
Motivation: In predicting HIV therapy outcomes, a critical clinical question is whether using historical information can enhance predictive capabilities compared with current or la…
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
Philippe Weitz, Masi Valkonen, Leslie Solorzano +38
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications. Advances in computing, deep learning, and availability…
High-resolution synthesis of high-density breast mammograms: Application to improved fairness in deep learning based mass detection
Lidia Garrucho, Kaisar Kushibar, Richard Osuala +7
Computer-aided detection systems based on deep learning have shown good performance in breast cancer detection. However, high-density breasts show poorer detection performance sinc…
NMR spectrum reconstruction as a pattern recognition problem
Amir Jahangiri, Xiao Han, Dmitry Lesovoy +4
A new deep neural network based on the WaveNet architecture (WNN) is presented, which is designed to grasp specific patterns in the NMR spectra. When trained at a fixed non-uniform…
Generative Aging of Brain Images with Diffeomorphic Registration
Jingru Fu, Antonios Tzortzakakis, José Barroso +3
Analyzing and predicting brain aging is essential for early prognosis and accurate diagnosis of cognitive diseases. The technique of neuroimaging, such as Magnetic Resonance Imagin…
Transcriptome-wide prediction of prostate cancer gene expression from histopathology images using co-expression based convolutional neural networks
Philippe Weitz, Yinxi Wang, Kimmo Kartasalo +5
Molecular phenotyping by gene expression profiling is common in contemporary cancer research and in molecular diagnostics. However, molecular profiling remains costly and resource…
CG-SENSE revisited: Results from the first ISMRM reproducibility challenge
Oliver Maier, Steven H. Baete, Alexander Fyrdahl +11
Purpose: The aim of this work is to shed light on the issue of reproducibility in MR image reconstruction in the context of a challenge. Participants had to recreate the results of…
The reliability of a deep learning model in clinical out-of-distribution MRI data: a multicohort study
Gustav Mårtensson, Daniel Ferreira, Tobias Granberg +22
Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in…
A Deep Learning based Pipeline for Efficient Oral Cancer Screening on Whole Slide Images
Jiahao Lu, Nataša Sladoje, Christina Runow Stark +3
Oral cancer incidence is rapidly increasing worldwide. The most important determinant factor in cancer survival is early diagnosis. To facilitate large scale screening, we propose…
Cartilage-binding antibodies induce pain through immune complex-mediated activation of neurons
Alex Bersellini Farinotti, Gustaf Wigerblad, Diana Nascimento +28
Rheumatoid arthritis-associated joint pain is frequently observed independent of disease activity, suggesting unidentified pain mechanisms. We demonstrate that antibodies binding t…
US-net for robust and efficient nuclei instance segmentation
Zhaoyang Xu, Faranak Sobhani, Carlos Fernandez Moro +1
We present a novel neural network architecture, US-Net, for robust nuclei instance segmentation in histopathology images. The proposed framework integrates the nuclei detection and…
AVRA: Automatic Visual Ratings of Atrophy from MRI images using Recurrent Convolutional Neural Networks
Gustav Mårtensson, Daniel Ferreira, Lena Cavallin +4
Quantifying the degree of atrophy is done clinically by neuroradiologists following established visual rating scales. For these assessments to be reliable the rater requires substa…
Variable domain N-linked glycosylation and negative surface charge are key features of monoclonal ACPA: implications for B-cell selection
Katy A. Lloyd, Johanna Steen, Khaled Amara +15
Autoreactive B cells have a central role in the pathogenesis of rheumatoid arthritis (RA), and recent findings have proposed that anti-citrullinated protein autoantibodies (ACPA) m…
Depressed serum IgM levels in SLE are restricted to defined subgroups
Caroline Gronwall, Uta Hardt, Johanna T. Gustafsson +9
Natural IgM autoantibodies have been proposed to convey protection from autoimmune pathogenesis. Herein, we investigated the IgM responses in 396 systemic lupus erythematosus (SLE)…