578 citations · 2.5k across the 216 of their papers we have counts for
29 papers · 1 filter
Continual Multiple Instance Learning for Hematologic Disease Diagnosis
Zahra Ebrahimi, Raheleh Salehi, Nassir Navab +2
The dynamic environment of laboratories and clinics, with streams of data arriving on a daily basis, requires regular updates of trained machine learning models for consistent perf…
Self-supervised Latent Space Optimization with Nebula Variational Coding
Yida Wang, David Joseph Tan, Nassir Navab +1
Deep learning approaches process data in a layer-by-layer way with intermediate (or latent) features. We aim at designing a general solution to optimize the latent manifolds to imp…
On Discprecncies between Perturbation Evaluations of Graph Neural Network Attributions
Razieh Rezaei, Alireza Dizaji, Ashkan Khakzar +3
Neural networks are increasingly finding their way into the realm of graphs and modeling relationships between features. Concurrently graph neural network explanation approaches ar…
PRISM: Progressive Restoration for Scene Graph-based Image Manipulation
Pavel Jahoda, Azade Farshad, Yousef Yeganeh +2
Scene graphs have emerged as accurate descriptive priors for image generation and manipulation tasks, however, their complexity and diversity of the shapes and relations of objects…
DISBELIEVE: Distance Between Client Models is Very Essential for Effective Local Model Poisoning Attacks
Indu Joshi, Priyank Upadhya, Gaurav Kumar Nayak +2
Federated learning is a promising direction to tackle the privacy issues related to sharing patients' sensitive data. Often, federated systems in the medical image analysis domain…
Improved Techniques for the Conditional Generative Augmentation of Clinical Audio Data
Mane Margaryan, Matthias Seibold, Indu Joshi +3
Data augmentation is a valuable tool for the design of deep learning systems to overcome data limitations and stabilize the training process. Especially in the medical domain, wher…
Adaptive Personlization in Federated Learning for Highly Non-i.i.d. Data
Yousef Yeganeh, Azade Farshad, Johann Boschmann +3
Federated learning (FL) is a distributed learning method that offers medical institutes the prospect of collaboration in a global model while preserving the privacy of their patien…
Unsupervised pre-training of graph transformers on patient population graphs
Chantal Pellegrini, Nassir Navab, Anees Kazi
Pre-training has shown success in different areas of machine learning, such as Computer Vision, Natural Language Processing (NLP), and medical imaging. However, it has not been ful…
Perception-Aligned AI Outputs: End-to-End Visual Prediction for Uncertainty Communication in Clinical Decision-Making
Mohammad Eslami, Solale Tabarestani, Saber Kazeminasab +9
Explainable Artificial Intelligence (XAI) is essential for trustworthy AI in healthcare, yet many existing methods rely on technical explanations that are difficult for clinicians…
Graph-in-Graph (GiG): Learning interpretable latent graphs in non-Euclidean domain for biological and healthcare applications
Kamilia Mullakaeva, Luca Cosmo, Anees Kazi +3
Graphs are a powerful tool for representing and analyzing unstructured, non-Euclidean data ubiquitous in the healthcare domain. Two prominent examples are molecule property predict…
Do Explanations Explain? Model Knows Best
Ashkan Khakzar, Pedram Khorsandi, Rozhin Nobahari +1
It is a mystery which input features contribute to a neural network's output. Various explanation (feature attribution) methods are proposed in the literature to shed light on the…
Unsupervised Pre-Training on Patient Population Graphs for Patient-Level Predictions
Chantal Pellegrini, Anees Kazi, Nassir Navab
Pre-training has shown success in different areas of machine learning, such as Computer Vision (CV), Natural Language Processing (NLP) and medical imaging. However, it has not been…
Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information
Yang Zhang, Ashkan Khakzar, Yawei Li +3
One principal approach for illuminating a black-box neural network is feature attribution, i.e. identifying the importance of input features for the network's prediction. The predi…
GKD: Semi-supervised Graph Knowledge Distillation for Graph-Independent Inference
Mahsa Ghorbani, Mojtaba Bahrami, Anees Kazi +3
The increased amount of multi-modal medical data has opened the opportunities to simultaneously process various modalities such as imaging and non-imaging data to gain a comprehens…
Patient-specific virtual spine straightening and vertebra inpainting: An automatic framework for osteoplasty planning
Christina Bukas, Bailiang Jian, Luis F. Rodriguez Venegas +9
Symptomatic spinal vertebral compression fractures (VCFs) often require osteoplasty treatment. A cement-like material is injected into the bone to stabilize the fracture, restore t…
RA-GCN: Graph Convolutional Network for Disease Prediction Problems with Imbalanced Data
Mahsa Ghorbani, Anees Kazi, Mahdieh Soleymani Baghshah +2
Disease prediction is a well-known classification problem in medical applications. GCNs provide a powerful tool for analyzing the patients' features relative to each other. This ca…
Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods
Ashkan Khakzar, Soroosh Baselizadeh, Nassir Navab
Saliency methods interpret the prediction of a neural network by showing the importance of input elements for that prediction. A popular family of saliency methods utilize gradient…
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…
Simultaneous imputation and disease classification in incomplete medical datasets using Multigraph Geometric Matrix Completion (MGMC)
Gerome Vivar, Anees Kazi, Hendrik Burwinkel +3
Large-scale population-based studies in medicine are a key resource towards better diagnosis, monitoring, and treatment of diseases. They also serve as enablers of clinical decisio…
Domain-specific loss design for unsupervised physical training: A new approach to modeling medical ML solutions
Hendrik Burwinkel, Holger Matz, Stefan Saur +6
Today, cataract surgery is the most frequently performed ophthalmic surgery in the world. The cataract, a developing opacity of the human eye lens, constitutes the world's most fre…
Decision Support for Intoxication Prediction Using Graph Convolutional Networks
Hendrik Burwinkel, Matthias Keicher, David Bani-Harouni +4
Every day, poison control centers (PCC) are called for immediate classification and treatment recommendations if an acute intoxication is suspected. Due to the time-sensitive natur…
Explicit Domain Adaptation with Loosely Coupled Samples
Oliver Scheel, Loren Schwarz, Nassir Navab +1
Transfer learning is an important field of machine learning in general, and particularly in the context of fully autonomous driving, which needs to be solved simultaneously for man…
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
Hannes Hase, Mohammad Farid Azampour, Maria Tirindelli +4
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input. Our approach combines state-of-t…
Peri-Diagnostic Decision Support Through Cost-Efficient Feature Acquisition at Test-Time
Gerome Vivar, Kamilia Mullakaeva, Andreas Zwergal +2
Computer-aided diagnosis (CADx) algorithms in medicine provide patient-specific decision support for physicians. These algorithms are usually applied after full acquisition of high…