Dynamic positron emission tomography imaging (dPET) provides temporally resolved images of a tracer enabling a quantitative measure of physiological processes.
However, the various types of breast tissue, such as glandular, fat, and lesions, differ in sound speed.
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).
For this purpose, longitudinal self-supervision schemes are explored on clinical longitudinal COVID-19 CT scans.
Algorithmic surgical workflow recognition is an ongoing research field and can be divided into laparoscopic (Internal) and operating room (External) analysis.
Existing automatic and interactive segmentation models for medical images only use data from a single time point (static).
no code implementations • 29 Jul 2021 • Matthias Keicher, Hendrik Burwinkel, David Bani-Harouni, Magdalini Paschali, Tobias Czempiel, Egon Burian, Marcus R. Makowski, Rickmer Braren, Nassir Navab, Thomas Wendler
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.
Image classification models deployed in the real world may receive inputs outside the intended data distribution.
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency.
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.
In this paper we introduce OperA, a transformer-based model that accurately predicts surgical phases from long video sequences.
In this paper we introduce the first reinforcement learning (RL) based robotic navigation method which utilizes ultrasound (US) images as an input.
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 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.
Radar signals have been dramatically increasing in complexity, limiting the source separation ability of traditional approaches.
The proposed method displays both promising image reconstruction quality and acquisition frequency when integrated for live ultrasound scanning.
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements.
In this paper, we propose a novel interpretation method tailored to histological Whole Slide Image (WSI) processing.
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.
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.
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.