Papers Phenotype classification
“Phenotype classification” 태그가 달린 논문 20편 · 필터 해제
Hierarchical Specialised Ensembles for Classification of Zebrafish Phenotypes Using the Selected Image Recognition Methods
We propose and evaluate three hierarchical ensemble setups for zebrafish phenotype classification from embryo images. In all setups, stage 1 uses a single four-class classifier to assign images to one of the exclusive ph…
Phenotype classificationHierarchical Classification via Cascading Feature Elimination: Application to Human Phenotype Ontology-Aligned Facial Phenotyping (FaceMesh2HPO)
FaceMesh2HPO is a framework for classifying facial phenotypic descriptors aligned with the Human Phenotype Ontology (HPO) to support clinical diagnosis. Using annotations from 124 clinicians across 10 disorders (107 HPO …
Phenotype classificationLatent World Recovery for Multimodal Learning with Missing Modalities
We study multimodal learning under missing modalities, with particular motivation from bioscience applications in which heterogeneous modalities are often only partially available when decisions need to be made. We propo…
Phenotype classificationRepresentation LearningResGene-T: A Tensor-Based Residual Network Approach for Genomic Prediction
In this work, we propose a new deep learning model for Genomic Prediction (GP), which involves correlating genotypic data with phenotypic. The genotypes are typically fed as a sequence of characters to the 1D-Convolution…
Phenotype classificationPhenoLIP: Integrating Phenotype Ontology Knowledge into Medical Vision-Language Pretraining
Recent progress in large-scale CLIP-like vision-language models(VLMs) has greatly advanced medical image analysis. However, most existing medical VLMs still rely on coarse image-text contrastive objectives and fail to ca…
Phenotype classificationKnowledge DistillationCross-Modal RetrievalGenerative diffusion models for agricultural AI: plant image generation, indoor-to-outdoor translation, and expert preference alignment
The success of agricultural artificial intelligence depends heavily on large, diverse, and high-quality plant image datasets, yet collecting such data in real field conditions is costly, labor intensive, and seasonally c…
Phenotype classificationImage GenerationA Weakly Supervised Transformer for Rare Disease Diagnosis and Subphenotyping from EHRs with Pulmonary Case Studies
Rare diseases affect an estimated 300-400 million people worldwide, yet individual conditions remain underdiagnosed and poorly characterized due to their low prevalence and limited clinician familiarity. Computational ph…
Phenotype classificationExpanding Training Data for Endoscopic Phenotyping of Eosinophilic Esophagitis
Eosinophilic esophagitis (EoE) is a chronic esophageal disorder marked by eosinophil-dominated inflammation. Diagnosing EoE usually involves endoscopic inspection of the esophageal mucosa and obtaining esophageal biopsie…
Diagnosticimage-classificationImage ClassificationPhenotype classificationCTPD: Cross-Modal Temporal Pattern Discovery for Enhanced Multimodal Electronic Health Records Analysis
Integrating multimodal Electronic Health Records (EHR) data, such as numerical time series and free-text clinical reports, has great potential in predicting clinical outcomes. However, prior work has primarily focused on…
cross-modal alignmentPhenotype classificationTrajGPT: Irregular Time-Series Representation Learning for Health Trajectory Analysis
In many domains, such as healthcare, time-series data is often irregularly sampled with varying intervals between observations. This poses challenges for classical time-series models that require equally spaced data. To …
Irregular Time SeriesPhenotype classificationRepresentation LearningTime Series+1Semi-supervised variational autoencoder for cell feature extraction in multiplexed immunofluorescence images
Advancements in digital imaging technologies have sparked increased interest in using multiplexed immunofluorescence (mIF) images to visualise and identify the interactions between specific immunophenotypes with the tumo…
Phenotype classificationTACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods ov…
ClusteringPhenotype classificationPredictionMerlin: A Vision Language Foundation Model for 3D Computed Tomography
Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current radiologist shortage, there is a large impetus to use artificial…
3D Semantic SegmentationComputed Tomography (CT)Cross-Modal RetrievalDisease Prediction+4sc-OTGM: Single-Cell Perturbation Modeling by Solving Optimal Mass Transport on the Manifold of Gaussian Mixtures
Influenced by breakthroughs in LLMs, single-cell foundation models are emerging. While these models show successful performance in cell type clustering, phenotype classification, and gene perturbation response prediction…
Inductive BiasPhenotype classificationRecommendation SystemsUnsupervised Domain Adaptation for Automated Knee Osteoarthritis Phenotype Classification
Purpose: The aim of this study was to demonstrate the utility of unsupervised domain adaptation (UDA) in automated knee osteoarthritis (OA) phenotype classification using a small dataset (n=50). Materials and Methods: Fo…
ClassificationDomain AdaptationPhenotype classificationSensitivity+2MedFuse: Multi-modal fusion with clinical time-series data and chest X-ray images
Multi-modal fusion approaches aim to integrate information from different data sources. Unlike natural datasets, such as in audio-visual applications, where samples consist of "paired" modalities, data in healthcare is o…
Mortality PredictionPhenotype classificationTime SeriesEnriching Unsupervised User Embedding via Medical Concepts
Clinical notes in Electronic Health Records (EHR) present rich documented information of patients to inference phenotype for disease diagnosis and study patient characteristics for cohort selection. Unsupervised user emb…
Mortality PredictionPhenotype classificationRetrievalA systematic evaluation of methods for cell phenotype classification using single-cell RNA sequencing data
Background: Single-cell RNA sequencing (scRNA-seq) yields valuable insights about gene expression and gives critical information about complex tissue cellular composition. In the analysis of single-cell RNA sequencing, t…
BIG-bench Machine LearningPhenotype classificationAnalysis | OPEN | Published: 17 June 2019 Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but pro…
BenchmarkingBIG-bench Machine LearningComputational PhenotypingLength-of-Stay prediction+3Multitask learning and benchmarking with clinical time series data
Health care is one of the most exciting frontiers in data mining and machine learning. Successful adoption of electronic health records (EHRs) created an explosion in digital clinical data available for analysis, but pro…
BenchmarkingBIG-bench Machine LearningComputational PhenotypingGeneral Classification+5