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Found 17 papers, 10 papers with code
From Histopathology Images to Cell Clouds: Learning Slide Representations with Hierarchical Cell Transformer
Thus, it remains an open question whether deep learning models can directly and effectively analyze WSIs from the semantic aspect of cell distributions.
From Pixels to Gigapixels: Bridging Local Inductive Bias and Long-Range Dependencies with Pixel-Mamba
Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making.
A Joint Representation Using Continuous and Discrete Features for Cardiovascular Diseases Risk Prediction on Chest CT Scans
In this work, we proposed a novel joint representation that integrates discrete quantitative biomarkers and continuous deep features extracted from chest CT scans.
End-to-end Multi-source Visual Prompt Tuning for Survival Analysis in Whole Slide Images
Survival analysis using pathology images poses a considerable challenge, as it requires the localization of relevant information from the multitude of tiles within whole slide images (WSIs).
Multimodal Neurodegenerative Disease Subtyping Explained by ChatGPT
Thus, we propose a multimodal framework that uses early-stage indicators such as imaging, genetics and clinical assessments to classify AD patients into subtypes at early stages.
Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
To accomplish this challenging task, first, a spectral sensitivity map is introduced to characterize the generalization weaknesses of models in the frequency domain.
When Neural Networks Fail to Generalize? A Model Sensitivity Perspective
Domain generalization (DG) aims to train a model to perform well in unseen domains under different distributions.
Regression Metric Loss: Learning a Semantic Representation Space for Medical Images
Regression plays an essential role in many medical imaging applications for estimating various clinical risk or measurement scores.
Cross-modal Attention for MRI and Ultrasound Volume Registration
In the past few years, convolutional neural networks (CNNs) have been proved powerful in extracting image features crucial for image registration.
AnaXNet: Anatomy Aware Multi-label Finding Classification in Chest X-ray
Radiologists usually observe anatomical regions of chest X-ray images as well as the overall image before making a decision.
Task-Oriented Low-Dose CT Image Denoising
The extensive use of medical CT has raised a public concern over the radiation dose to the patient.
GaitSet: Cross-view Gait Recognition through Utilizing Gait as a Deep Set
In this paper, we present a novel perspective that utilizes gait as a deep set, which means that a set of gait frames are integrated by a global-local fused deep network inspired by the way our left- and right-hemisphere processes information to learn information that can be used in identification.
Robustified Domain Adaptation
Unsupervised domain adaptation (UDA) is widely used to transfer knowledge from a labeled source domain to an unlabeled target domain with different data distribution.
Deep Learning Predicts Cardiovascular Disease Risks from Lung Cancer Screening Low Dose Computed Tomography
Cancer patients have a higher risk of cardiovascular disease (CVD) mortality than the general population.
Integrative Analysis for COVID-19 Patient Outcome Prediction
Management of high-risk patients with early intervention is a key to lower the fatality rate of COVID-19 pneumonia, as a majority of patients recover naturally.
Towards a Precipitation Bias Corrector against Noise and Maldistribution
To the best of our knowledge, it is the first expert-free models for bias correction.
GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition
In this paper we present a novel perspective, where a gait is regarded as a set consisting of independent frames.
Ranked #2 on
Multiview Gait Recognition
on OU-MVLP
