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Found 20 papers, 7 papers with code
Prompt-driven Universal Model for View-Agnostic Echocardiography Analysis
Echocardiography segmentation for cardiac analysis is time-consuming and resource-intensive due to the variability in image quality and the necessity to process scans from various standard views.
SP$^2$OT: Semantic-Regularized Progressive Partial Optimal Transport for Imbalanced Clustering
To be more precise, we employ the strategy of majorization to reformulate the SP$^2$OT problem into a Progressive Partial Optimal Transport problem, which can be transformed into an unbalanced optimal transport problem with augmented constraints and can be solved efficiently by a fast matrix scaling algorithm.
Cardiac Magnetic Resonance 2D+T Short- and Long-axis Segmentation via Spatio-temporal SAM Adaptation
Accurate 2D+T myocardium segmentation in cine cardiac magnetic resonance (CMR) scans is essential to analyze LV motion throughout the cardiac cycle comprehensively.
Conditional Score-Based Diffusion Model for Cortical Thickness Trajectory Prediction
Alzheimer's Disease (AD) is a neurodegenerative condition characterized by diverse progression rates among individuals, with changes in cortical thickness (CTh) closely linked to its progression.
Medical Image Synthesis via Fine-Grained Image-Text Alignment and Anatomy-Pathology Prompting
To address these challenges, we propose a novel medical image synthesis model that leverages fine-grained image-text alignment and anatomy-pathology prompts to generate highly detailed and accurate synthetic medical images.
P$^2$OT: Progressive Partial Optimal Transport for Deep Imbalanced Clustering
Deep clustering, which learns representation and semantic clustering without labels information, poses a great challenge for deep learning-based approaches.
CohortGPT: An Enhanced GPT for Participant Recruitment in Clinical Study
Participant recruitment based on unstructured medical texts such as clinical notes and radiology reports has been a challenging yet important task for the cohort establishment in clinical research.
Exploring Multimodal Approaches for Alzheimer's Disease Detection Using Patient Speech Transcript and Audio Data
As AD impairs the patient's language understanding and expression ability, the speech of AD patients can serve as an indicator of this disease.
BiomedGPT: A Unified and Generalist Biomedical Generative Pre-trained Transformer for Vision, Language, and Multimodal Tasks
Conventional task- and modality-specific artificial intelligence (AI) models are inflexible in real-world deployment and maintenance for biomedicine.
Ranked #1 on
Text Summarization
on MeQSum
Coarse-to-fine Knowledge Graph Domain Adaptation based on Distantly-supervised Iterative Training
In this framework, we apply distant-supervision on cross-domain knowledge graph adaptation.
2nd Place Solution to Google Landmark Retrieval 2020
Using this method, we got a public score of 0. 40176 and a private score of 0. 36278 and achieved 2nd place in the Google Landmark Retrieval Competition 2020.
Cascaded Sparse Feature Propagation Network for Interactive Segmentation
We aim to tackle the problem of point-based interactive segmentation, in which the key challenge is to propagate the user-provided annotations to unlabeled regions efficiently.
Ranked #4 on
Interactive Segmentation
on SBD
Development and Validation of a Deep Learning Model for Prediction of Severe Outcomes in Suspected COVID-19 Infection
Predictive risk scores for COVID-19 severe outcomes ("CO-RISK" score) were derived from model output and evaluated on the testing dataset, as well as compared to human performance.
Manipulating the anisotropic phase separation in strained VO2 epitaxial films by nanoscale ion-implantation
Manipulating the strain induced poly-domains and phase transition in correlated oxide material are important for high performance devices fabrication.
Materials Science Strongly Correlated Electrons
Deep Metric Learning-based Image Retrieval System for Chest Radiograph and its Clinical Applications in COVID-19
These results demonstrate our deep metric learning based image retrieval model is highly efficient in the CXR retrieval, diagnosis and prognosis, and thus has great clinical value for the treatment and management of COVID-19 patients.
Deep Learning-based Four-region Lung Segmentation in Chest Radiography for COVID-19 Diagnosis
A segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used, which are the clinical landmarks to separate the upper and lower lungs.
Self-supervised Dynamic CT Perfusion Image Denoising with Deep Neural Networks
It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis.
Zooming into Face Forensics: A Pixel-level Analysis
The stunning progress in face manipulation methods has made it possible to synthesize realistic fake face images, which poses potential threats to our society.
2nd Place and 2nd Place Solution to Kaggle Landmark Recognition andRetrieval Competition 2019
We present a retrieval based system for landmark retrieval and recognition challenge. There are five parts in retrieval competition system, including feature extraction and matching to get candidates queue; database augmentation and query extension searching; reranking from recognition results and local feature matching.
Multi-Estimator Full Left Ventricle Quantification through Ensemble Learning
The framework combines two 1st-level modules: direct estimation module and a segmentation module.
