Papers Survival Prediction
“Survival Prediction” 태그가 달린 논문 233편 · 필터 해제
Benchmarking Waitlist Mortality Prediction in Heart Transplantation Through Time-to-Event Modeling using New Longitudinal UNOS Dataset
Decisions about managing patients on the heart transplant waitlist are currently made by committees of doctors who consider multiple factors, but the process remains largely ad-hoc. With the growing volume of longitudina…
BenchmarkingDecision MakingMortality PredictionSurvival PredictionOTSurv: A Novel Multiple Instance Learning Framework for Survival Prediction with Heterogeneity-aware Optimal Transport
Survival prediction using whole slide images (WSIs) can be formulated as a multiple instance learning (MIL) problem. However, existing MIL methods often fail to explicitly capture pathological heterogeneity within WSIs, …
Multiple Instance LearningSurvival Predictionwhole slide imagesImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer
Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learning-based predictive models rely primarily on pre-treatment imaging to pr…
AnatomySurvival PredictionAny-to-Any Learning in Computational Pathology via Triplet Multimodal Pretraining
Recent advances in computational pathology and artificial intelligence have significantly enhanced the utilization of gigapixel whole-slide images and and additional modalities (e.g., genomics) for pathological diagnosis…
Survival PredictionTripletwhole slide imagesMultimodal Cancer Survival Analysis via Hypergraph Learning with Cross-Modality Rebalance
Multimodal pathology-genomic analysis has become increasingly prominent in cancer survival prediction. However, existing studies mainly utilize multi-instance learning to aggregate patch-level features, neglecting the in…
Survival AnalysisSurvival PredictionMultimodal Survival Modeling in the Age of Foundation Models
The Cancer Genome Atlas (TCGA) has enabled novel discoveries and served as a large-scale reference through its harmonized genomics, clinical, and image data. Prior studies have trained bespoke cancer survival prediction …
HallucinationSurvival PredictionText SummarizationRobSurv: Vector Quantization-Based Multi-Modal Learning for Robust Cancer Survival Prediction
Cancer survival prediction using multi-modal medical imaging presents a critical challenge in oncology, mainly due to the vulnerability of deep learning models to noise and protocol variations across imaging centers. Cur…
PrognosisQuantizationSurvival PredictionBeyond Cox Models: Assessing the Performance of Machine-Learning Methods in Non-Proportional Hazards and Non-Linear Survival Analysis
Survival analysis often relies on Cox models, assuming both linearity and proportional hazards (PH). This study evaluates machine and deep learning methods that relax these constraints, comparing their performance with p…
Survival AnalysisSurvival PredictionTime-to-event prediction for grouped variables using Exclusive Lasso
The integration of high-dimensional genomic data and clinical data into time-to-event prediction models has gained significant attention due to the growing availability of these datasets. Traditionally, a Cox regression …
feature selectionregressionSurvival PredictionTime-to-Event PredictionMIL vs. Aggregation: Evaluating Patient-Level Survival Prediction Strategies Using Graph-Based Learning
Oncologists often rely on a multitude of data, including whole-slide images (WSIs), to guide therapeutic decisions, aiming for the best patient outcome. However, predicting the prognosis of cancer patients can be a chall…
Multiple Instance LearningPrognosisSurvival Predictionwhole slide imagesAdaMHF: Adaptive Multimodal Hierarchical Fusion for Survival Prediction
The integration of pathologic images and genomic data for survival analysis has gained increasing attention with advances in multimodal learning. However, current methods often ignore biological characteristics, such as …
PredictionSurvival AnalysisSurvival PredictionVGAT: A Cancer Survival Analysis Framework Transitioning from Generative Visual Question Answering to Genomic Reconstruction
Multimodal learning combining pathology images and genomic sequences enhances cancer survival analysis but faces clinical implementation barriers due to limited access to genomic sequencing in under-resourced regions. To…
Generative Visual Question AnsweringQuestion AnsweringSurvival AnalysisSurvival Prediction+3PathoHR: Breast Cancer Survival Prediction on High-Resolution Pathological Images
Breast cancer survival prediction in computational pathology presents a remarkable challenge due to tumor heterogeneity. For instance, different regions of the same tumor in the pathology image can show distinct morpholo…
PredictionRepresentation LearningSurvival Predictionwhole slide imagesDisentangled and Interpretable Multimodal Attention Fusion for Cancer Survival Prediction
To improve the prediction of cancer survival using whole-slide images and transcriptomics data, it is crucial to capture both modality-shared and modality-specific information. However, multimodal frameworks often entang…
DisentanglementSurvival Predictionwhole slide imagesHySurvPred: Multimodal Hyperbolic Embedding with Angle-Aware Hierarchical Contrastive Learning and Uncertainty Constraints for Survival Prediction
Multimodal learning that integrates histopathology images and genomic data holds great promise for cancer survival prediction. However, existing methods face key limitations: 1) They rely on multimodal mapping and metric…
Contrastive LearningModel OptimizationSurvival PredictionBayesian Cox model with graph-structured variable selection priors for multi-omics biomarker identification
An important goal in cancer research is the survival prognosis of a patient based on a minimal panel of genomic and molecular markers such as genes or proteins. Purely data-driven models without any biological knowledge …
PrognosisSurvival PredictionVariable SelectionFrom Pixels to Histopathology: A Graph-Based Framework for Interpretable Whole Slide Image Analysis
The histopathological classification of whole-slide images (WSIs) is a fundamental task in digital pathology; yet it requires extensive time and expertise from specialists. While deep learning methods show promising resu…
DiagnosticGraph AttentionSurvival Predictionwhole slide imagesMulti-Modal Mamba Modeling for Survival Prediction (M4Survive): Adapting Joint Foundation Model Representations
Accurate survival prediction in oncology requires integrating diverse imaging modalities to capture the complex interplay of tumor biology. Traditional single-modality approaches often fail to leverage the complementary …
Computational EfficiencyMambaPredictionSurvival PredictionPrototype-Guided Cross-Modal Knowledge Enhancement for Adaptive Survival Prediction
Histo-genomic multimodal survival prediction has garnered growing attention for its remarkable model performance and potential contributions to precision medicine. However, a significant challenge in clinical practice ar…
Survival PredictionRobust Multimodal Survival Prediction with the Latent Differentiation Conditional Variational AutoEncoder
The integrative analysis of histopathological images and genomic data has received increasing attention for survival prediction of human cancers. However, the existing studies always hold the assumption that full modalit…
Survival Predictionwhole slide images