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Found 10 papers, 4 papers with code
LOTUS: Learning to Optimize Task-based US representations
Anatomical segmentation of organs in ultrasound images is essential to many clinical applications, particularly for diagnosis and monitoring.
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.
Spotlight on nerves: Portable multispectral optoacoustic imaging of peripheral nerve vascularization and morphology
Various morphological and functional parameters of peripheral nerves and their vascular supply are indicative of pathological changes due to injury or disease.
CACTUSS: Common Anatomical CT-US Space for US examinations
Abdominal aortic aneurysm (AAA) is a vascular disease in which a section of the aorta enlarges, weakening its walls and potentially rupturing the vessel.
Rethinking Ultrasound Augmentation: A Physics-Inspired Approach
Medical Ultrasound (US), despite its wide use, is characterized by artifacts and operator dependency.
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
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.
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.
