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Found 12 papers, 4 papers with code
A Bayesian Topic Model for Human-Evaluated Interpretability
Given a nonparametric topic model, we can include weakly-supervised input using novel modifications to the nonparametric generative model.
STACT-Time: Spatio-Temporal Cross Attention for Cine Thyroid Ultrasound Time Series Classification
By leveraging self-attention and cross-attention mechanisms, our model captures the rich temporal and spatial context of US cine clips while enhancing feature representation through segmentation-guided learning.
DSA-NRP: No-Reflow Prediction from Angiographic Perfusion Dynamics in Stroke EVT
Following successful large-vessel recanalization via endovascular thrombectomy (EVT) for acute ischemic stroke (AIS), some patients experience a complication known as no-reflow, defined by persistent microvascular hypoperfusion that undermines tissue recovery and worsens clinical outcomes.
CytoFM: The first cytology foundation model
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature.
Prototype-Guided Diffusion for Digital Pathology: Achieving Foundation Model Performance with Minimal Clinical Data
Foundation models in digital pathology use massive datasets to learn useful compact feature representations of complex histology images.
Evaluation Of P300 Speller Performance Using Large Language Models Along With Cross-Subject Training
A recurring theme in P300-based research is enhancing performance to enable faster subject interaction.
High Performance P300 Spellers Using GPT2 Word Prediction With Cross-Subject Training
Amyotrophic lateral sclerosis (ALS) severely impairs patients' ability to communicate, often leading to a decline in their quality of life within a few years of diagnosis.
Multi-site, Multi-domain Airway Tree Modeling (ATM'22): A Public Benchmark for Pulmonary Airway Segmentation
In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolution.
Toward heterogeneous information fusion: bipartite graph convolutional networks for in silico drug repurposing
Unlike conventional graph convolution networks always assuming the same node attributes in a global graph, our approach models interdomain information fusion with bipartite graph convolution operation.
Semi-supervised learning based on generative adversarial network: a comparison between good GAN and bad GAN approach
Recently, semi-supervised learning methods based on generative adversarial networks (GANs) have received much attention.
Semi-supervised Rare Disease Detection Using Generative Adversarial Network
Rare diseases affect a relatively small number of people, which limits investment in research for treatments and cures.
Source-LDA: Enhancing probabilistic topic models using prior knowledge sources
The approach taken is to complement the existing topic distributions over words with a known distribution based on a predefined set of topics.
