Search Results for author:
Found 9 papers, 4 papers with code
Consisaug: A Consistency-based Augmentation for Polyp Detection in Endoscopy Image Analysis
Colorectal cancer (CRC), which frequently originates from initially benign polyps, remains a significant contributor to global cancer-related mortality.
DeViDe: Faceted medical knowledge for improved medical vision-language pre-training
These descriptions outline general visual characteristics of diseases in radiographs, and when combined with abstract definitions and radiology reports, provide a holistic snapshot of knowledge.
An Interpretable Cross-Attentive Multi-modal MRI Fusion Framework for Schizophrenia Diagnosis
Both functional and structural magnetic resonance imaging (fMRI and sMRI) are widely used for the diagnosis of mental disorder.
Learning Anatomically Consistent Embedding for Chest Radiography
Self-supervised learning (SSL) approaches have recently shown substantial success in learning visual representations from unannotated images.
CoC-GAN: Employing Context Cluster for Unveiling a New Pathway in Image Generation
We introduce this model with the novel structure as the Context Clustering Generative Adversarial Network (CoC-GAN), which offers a distinctive viewpoint in the domain of feature aggregating and dispatching.
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
To tackle the source data-absent problem, we present a novel two-stage source-free domain adaptation (SFDA) framework for medical image segmentation, where only a well-trained source segmentation model and unlabeled target data are available during domain adaptation.
Angle Basis: a Generative Model and Decomposition for Functional Connectivity
We also find that both the decomposition and its residual have approximately equal predictive value, and when combined into an ensemble, exceed the AUC of FC-based prediction by up to 5%.
ImageNomer: description of a functional connectivity and omics analysis tool and case study identifying a race confound
The reason is that it is too slow and cumbersome to use a programming interface to create all the necessary visualizations required to identify all correlations, confounding effects, or quality control problems in a dataset.
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data.
