3.1k citations · 4.3k across the 48 of their papers we have counts for
57 papers
SegSTRONG-C: Segmenting Surgical Tools Robustly On Non-adversarial Generated Corruptions -- An EndoVis'24 Challenge
Hao Ding, Yuqian Zhang, Tuxun Lu +39
Surgical data science has seen rapid advancement with the excellent performance of end-to-end deep neural networks (DNNs). Despite their successes, DNNs have been proven susceptibl…
FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare
Karim Lekadir, Aasa Feragen, Abdul Joseph Fofanah +117
Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. I…
LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching
Duy M. H. Nguyen, Hoang Nguyen, Nghiem T. Diep +9
Obtaining large pre-trained models that can be fine-tuned to new tasks with limited annotated samples has remained an open challenge for medical imaging data. While pre-trained dee…
LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset
Yiping Jiao, Jeroen van der Laak, Shadi Albarqouni +18
We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzen (China). The competition required participants to…
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Duy M. H. Nguyen, Hoang Nguyen, Mai T. N. Truong +7
Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs i…
What can we learn about a generated image corrupting its latent representation?
Agnieszka Tomczak, Aarushi Gupta, Slobodan Ilic +2
Generative adversarial networks (GANs) offer an effective solution to the image-to-image translation problem, thereby allowing for new possibilities in medical imaging. They can tr…
FLamby: Datasets and Benchmarks for Cross-Silo Federated Learning in Realistic Healthcare Settings
Jean Ogier du Terrail, Samy-Safwan Ayed, Edwige Cyffers +21
Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-s…
Transformer based Models for Unsupervised Anomaly Segmentation in Brain MR Images
Ahmed Ghorbel, Ahmed Aldahdooh, Shadi Albarqouni +1
The quality of patient care associated with diagnostic radiology is proportionate to a physician workload. Segmentation is a fundamental limiting precursor to both diagnostic and t…
Anomaly-aware multiple instance learning for rare anemia disorder classification
Salome Kazeminia, Ario Sadafi, Asya Makhro +3
Deep learning-based classification of rare anemia disorders is challenged by the lack of training data and instance-level annotations. Multiple Instance Learning (MIL) has shown to…
Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification
Raheleh Salehi, Ario Sadafi, Armin Gruber +4
Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures,…
Virtual embeddings and self-consistency for self-supervised learning
Tariq Bdair, Hossam Abdelhamid, Nassir Navab +1
Self-supervised Learning (SSL) has recently gained much attention due to the high cost and data limitation in the training of supervised learning models. The current paradigm in th…
FedNorm: Modality-Based Normalization in Federated Learning for Multi-Modal Liver Segmentation
Tobias Bernecker, Annette Peters, Christopher L. Schlett +5
Given the high incidence and effective treatment options for liver diseases, they are of great socioeconomic importance. One of the most common methods for analyzing CT and MRI ima…
Sickle Cell Disease Severity Prediction from Percoll Gradient Images using Graph Convolutional Networks
Ario Sadafi, Asya Makhro, Leonid Livshits +4
Sickle cell disease (SCD) is a severe genetic hemoglobin disorder that results in premature destruction of red blood cells. Assessment of the severity of the disease is a challengi…
The Federated Tumor Segmentation (FeTS) Challenge
Sarthak Pati, Ujjwal Baid, Maximilian Zenk +29
This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standar…
Fourier Transform of Percoll Gradients Boosts CNN Classification of Hereditary Hemolytic Anemias
Ario Sadafi, Lucía María Moya Sans, Asya Makhro +5
Hereditary hemolytic anemias are genetic disorders that affect the shape and density of red blood cells. Genetic tests currently used to diagnose such anemias are expensive and una…
FedDis: Disentangled Federated Learning for Unsupervised Brain Pathology Segmentation
Cosmin I. Bercea, Benedikt Wiestler, Daniel Rueckert +1
In recent years, data-driven machine learning (ML) methods have revolutionized the computer vision community by providing novel efficient solutions to many unsolved (medical) image…
Semi-Supervised Federated Peer Learning for Skin Lesion Classification
Tariq Bdair, Nassir Navab, Shadi Albarqouni
Globally, Skin carcinoma is among the most lethal diseases. Millions of people are diagnosed with this cancer every year. Sill, early detection can decrease the medication cost and…
An Uncertainty-Driven GCN Refinement Strategy for Organ Segmentation
Roger D. Soberanis-Mukul, Nassir Navab, Shadi Albarqouni
Organ segmentation in CT volumes is an important pre-processing step in many computer assisted intervention and diagnosis methods. In recent years, convolutional neural networks ha…
Polyp-artifact relationship analysis using graph inductive learned representations
Roger D. Soberanis-Mukul, Shadi Albarqouni, Nassir Navab
The diagnosis process of colorectal cancer mainly focuses on the localization and characterization of abnormal growths in the colon tissue known as polyps. Despite recent advances…
Inverse Distance Aggregation for Federated Learning with Non-IID Data
Yousef Yeganeh, Azade Farshad, Nassir Navab +1
Federated learning (FL) has been a promising approach in the field of medical imaging in recent years. A critical problem in FL, specifically in medical scenarios is to have a more…
Attention based Multiple Instance Learning for Classification of Blood Cell Disorders
Ario Sadafi, Asya Makhro, Anna Bogdanova +4
Red blood cells are highly deformable and present in various shapes. In blood cell disorders, only a subset of all cells is morphologically altered and relevant for the diagnosis.…
Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI
Christoph Baur, Benedikt Wiestler, Shadi Albarqouni +1
Brain pathologies can vary greatly in size and shape, ranging from few pixels (i.e. MS lesions) to large, space-occupying tumors. Recently proposed Autoencoder-based methods for un…
Autoencoders for Unsupervised Anomaly Segmentation in Brain MR Images: A Comparative Study
Christoph Baur, Stefan Denner, Benedikt Wiestler +2
Deep unsupervised representation learning has recently led to new approaches in the field of Unsupervised Anomaly Detection (UAD) in brain MRI. The main principle behind these work…
6D Camera Relocalization in Ambiguous Scenes via Continuous Multimodal Inference
Mai Bui, Tolga Birdal, Haowen Deng +4
We present a multimodal camera relocalization framework that captures ambiguities and uncertainties with continuous mixture models defined on the manifold of camera poses. In highl…