Related papers: Benchmark problems for transcranial ultrasound sim…
Low-intensity transcranial focused ultrasound (tFUS) is rapidly emerging as a transformative non-invasive brain stimulation (NIBS) modality characterized by high spatial resolution and ability to target deep brain circuits. Unlike…
Electroencephalography (EEG) foundation models have recently emerged as a promising paradigm for brain-computer interfaces (BCIs), aiming to learn transferable neural representations from large-scale heterogeneous recordings. Despite rapid…
In fluid-filled microchannels embedded in solid devices and driven by MHz ultrasound transducers, the thickness of the viscous boundary layer in the fluid near the confining walls is typically 3 to 4 orders of magnitude smaller than the…
Cross-modal associations between voice and face from a person can be learnt algorithmically, which can benefit a lot of applications. The problem can be defined as voice-face matching and retrieval tasks. Much research attention has been…
Neuronavigation is widely used in biomedical research and interventions to guide the precise placement of instruments around the head to support procedures such as transcranial magnetic stimulation. Traditional systems, however, rely on…
Over the past decade, U-Net has been the dominant architecture in medical image segmentation, leading to the development of thousands of U-shaped variants. Despite its widespread adoption, there is still no comprehensive benchmark to…
Medical audio data is difficult to collect due to privacy regulations and high annotation costs arising from domain expertise. Thus, existing benchmarks tend to underrepresent complex medical audio scenarios. To address this challenge, we…
The current methods of assessing tendon health such as clinical examination, imaging techniques, and implanted pressure sensors, are often based on a subjective assessment or are not accurate enough, are extremely expensive, or are limited…
Acoustic propagation models are widely used in numerous oceanic and other underwater applications. Most conventional models are approximate solutions of the acoustic wave equation, and require accurate environmental knowledge to be…
Computational biomechanics of the brain for neurosurgery is an emerging area of research recently gaining in importance and practical applications. This review paper presents the contributions of the Intelligent Systems for Medicine…
Robust decoding and classification of brain patterns measured with electroencephalography (EEG) remains a major challenge for real-world (i.e. outside scientific lab and medical facilities) brain-computer interface (BCI) applications due to…
We present the first set of benchmark problems for phase field models that are being developed by the Center for Hierarchical Materials Design (CHiMaD) and the National Institute of Standards and Technology (NIST). While many scientific…
Simulations of three dimensional ultrasound propagation in heterogeneous media are computationally intensive due to the constraints arising from the large size of the domain, which is on the order of hundreds of wavelengths, and the small…
Ultrasound images vary widely across scanners, operators, and anatomical targets, which often causes models trained in one setting to generalize poorly to new hospitals and clinical conditions. The Foundation Model Challenge for Ultrasound…
In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different…
Focused ultrasound spinal cord neuromodulation studies have demonstrated spinal cord neuromodulation in small animals. The safe and efficacious translation of these approaches to human scale requires an understanding of ultrasound…
The development of acoustic simulation workflows in the time-domain description is essential for predicting the sound of aeroacoustic or other transient acoustic effects. A common practice for noise mitigation is using absorbers. The…
Tabular Foundation Models have recently established the state of the art in supervised tabular learning, by leveraging pretraining to learn generalizable representations of numerical and categorical structured data. However, they lack…
Determining brain hemodynamics plays a critical role in the diagnosis and treatment of various cerebrovascular diseases. In this work, we put forth a physics-informed deep learning framework that augments sparse clinical measurements with…
Generating 3D city models rapidly is crucial for many applications. Monocular height estimation is one of the most efficient and timely ways to obtain large-scale geometric information. However, existing works focus primarily on training…