Search Results for author:
Found 26 papers, 7 papers with code
Foundation Models in Radiology: What, How, When, Why and Why Not
Given the potentially transformative impact that foundation models can have on the field of radiology, this review aims to establish a standardized terminology concerning foundation models, with a specific focus on the requirements of training data, model training paradigms, model capabilities, and evaluation strategies.
SOE: SO(3)-Equivariant 3D MRI Encoding
This approach requires moving beyond traditional representation learning methods, as we need a representation vector space that allows for the application of the same SO(3) operation in that space.
Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive Models for Neuroimaging
To this end, we propose to model the subject weights as a linear combination of the eigenbases of a spectral population graph that captures the similarity of factors across subjects.
Merlin: A Vision Language Foundation Model for 3D Computed Tomography
However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision.
CheXagent: Towards a Foundation Model for Chest X-Ray Interpretation
However, developing FMs that can accurately interpret CXRs is challenging due to the (1) limited availability of large-scale vision-language datasets in the medical image domain, (2) lack of vision and language encoders that can capture the complexities of medical data, and (3) absence of evaluation frameworks for benchmarking the abilities of FMs on CXR interpretation.
Self-Supervised Learning for Physiologically-Based Pharmacokinetic Modeling in Dynamic PET
Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.
Investigating Pulse-Echo Sound Speed Estimation in Breast Ultrasound with Deep Learning
However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed.
Bridging the Gap between Deep Learning and Hypothesis-Driven Analysis via Permutation Testing
A fundamental approach in neuroscience research is to test hypotheses based on neuropsychological and behavioral measures, i. e., whether certain factors (e. g., related to life events) are associated with an outcome (e. g., depression).
Longitudinal Self-Supervision for COVID-19 Pathology Quantification
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
Surgical Workflow Recognition: from Analysis of Challenges to Architectural Study
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.
Interactive Segmentation for COVID-19 Infection Quantification on Longitudinal CT scans
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
U-GAT: Multimodal Graph Attention Network for COVID-19 Outcome Prediction
Specifically, we introduce a multimodal similarity metric to build a population graph for clustering patients and an image-based end-to-end Graph Attention Network to process this graph and predict the COVID-19 patient outcomes: admission to ICU, need for ventilation and mortality.
Confidence-based Out-of-Distribution Detection: A Comparative Study and Analysis
Image classification models deployed in the real world may receive inputs outside the intended data distribution.
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency.
Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs
Chest computed tomography (CT) has played an essential diagnostic role in assessing patients with COVID-19 by showing disease-specific image features such as ground-glass opacity and consolidation.
OperA: Attention-Regularized Transformers for Surgical Phase Recognition
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
Ultrasound-Guided Robotic Navigation with Deep Reinforcement Learning
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.
TeCNO: Surgical Phase Recognition with Multi-Stage Temporal Convolutional Networks
Automatic surgical phase recognition is a challenging and crucial task with the potential to improve patient safety and become an integral part of intra-operative decision-support systems.
Ranked #4 on
Surgical phase recognition
on Cholec80
Radar Emitter Classification with Attribute-specific Recurrent Neural Networks
Radar pulse streams exhibit increasingly complex temporal patterns and can no longer rely on a purely value-based analysis of the pulse attributes for the purpose of emitter classification.
Signal Clustering with Class-independent Segmentation
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.
End-to-End Learning-Based Ultrasound Reconstruction
The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.
3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.
Deep Learning Under the Microscope: Improving the Interpretability of Medical Imaging Neural Networks
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing.
Data Augmentation with Manifold Exploring Geometric Transformations for Increased Performance and Robustness
Compared with traditional augmentation methods, and with images synthesized by Generative Adversarial Networks our method not only achieves state-of-the-art performance but also significantly improves the network's robustness.
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
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 in extreme cases of noise, outliers and ambiguous input data.
Learning Robust Hash Codes for Multiple Instance Image Retrieval
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 large-scale retrieval.
