Papers Survival Prediction
“Survival Prediction” 태그가 달린 논문 233편 · 필터 해제
Survival Prediction in Lung Cancer through Multi-Modal Representation Learning
Survival prediction is a crucial task associated with cancer diagnosis and treatment planning. This paper presents a novel approach to survival prediction by harnessing comprehensive information from CT and PET scans, al…
Representation LearningSurvival PredictionDRIM: Learning Disentangled Representations from Incomplete Multimodal Healthcare Data
Real-life medical data is often multimodal and incomplete, fueling the growing need for advanced deep learning models capable of integrating them efficiently. The use of diverse modalities, including histopathology slide…
Contrastive LearningPrognosisSurvival PredictionSeqRisk: Transformer-augmented latent variable model for improved survival prediction with longitudinal data
In healthcare, risk assessment of different patient outcomes has for long time been based on survival analysis, i.e.\ modeling time-to-event associations. However, conventional approaches rely on data from a single time-…
Survival AnalysisSurvival PredictionEnd-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). Current methods typically res…
DecoderSurvival AnalysisSurvival PredictionVisual Prompt Tuning+1Multi-modal Intermediate Feature Interaction AutoEncoder for Overall Survival Prediction of Esophageal Squamous Cell Cancer
Survival prediction for esophageal squamous cell cancer (ESCC) is crucial for doctors to assess a patient's condition and tailor treatment plans. The application and development of multi-modal deep learning in this field…
PrognosisSurvival PredictionWave-LSTM: Multi-scale analysis of somatic whole genome copy number profiles
Changes in the number of copies of certain parts of the genome, known as copy number alterations (CNAs), due to somatic mutation processes are a hallmark of many cancers. This genomic complexity is known to be associated…
Survival PredictionImproving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model t…
Cancer ClassificationLung Cancer DiagnosisSurvival PredictionHistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
Machine learning in computational pathology (CPath) often aggregates patch-level predictions from multi-gigapixel Whole Slide Images (WSIs) to generate WSI-level prediction scores for crucial tasks such as survival predi…
PredictionRetrievalSurvival AnalysisSurvival Prediction+1Improving Mortality Prediction After Radiotherapy with Large Language Model Structuring of Large-Scale Unstructured Electronic Health Records
Accurate survival prediction in radiotherapy (RT) is critical for optimizing treatment decisions. This study developed and validated the RT-Surv framework, which integrates general-domain, open-source large language mode…
Language ModelingLanguage ModellingLarge Language ModelMortality Prediction+1M2EF-NNs: Multimodal Multi-instance Evidence Fusion Neural Networks for Cancer Survival Prediction
Accurate cancer survival prediction is crucial for assisting clinical doctors in formulating treatment plans. Multimodal data, including histopathological images and genomic data, offer complementary and comprehensive in…
PredictionSurvival PredictionTowards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
Foundation models pretrained on large-scale datasets are revolutionizing the field of computational pathology (CPath). The generalization ability of foundation models is crucial for the success in various downstream clin…
Knowledge DistillationQuestion AnsweringRepresentation LearningSelf-Knowledge Distillation+3Normative Diffusion Autoencoders: Application to Amyotrophic Lateral Sclerosis
Predicting survival in Amyotrophic Lateral Sclerosis (ALS) is a challenging task. Magnetic resonance imaging (MRI) data provide in vivo insight into brain health, but the low prevalence of the condition and resultant dat…
Survival PredictionWSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Whole slide imaging is routinely adopted for carcinoma diagnosis and prognosis. Abundant experience is required for pathologists to achieve accurate and reliable diagnostic results of whole slide images (WSI). The huge s…
DiagnosticGenerative Visual Question AnsweringPrognosisQuestion Answering+5SCMIL: Sparse Context-aware Multiple Instance Learning for Predicting Cancer Survival Probability Distribution in Whole Slide Images
Cancer survival prediction is a challenging task that involves analyzing of the tumor microenvironment within Whole Slide Image (WSI). Previous methods cannot effectively capture the intricate interaction features among …
Multiple Instance LearningPredictionSurvival Predictionwhole slide imagesMultimodal Prototyping for cancer survival prediction
Multimodal survival methods combining gigapixel histology whole-slide images (WSIs) and transcriptomic profiles are particularly promising for patient prognostication and stratification. Current approaches involve tokeni…
PredictionSurvival Predictionwhole slide imagesMultimodal Cross-Task Interaction for Survival Analysis in Whole Slide Pathological Images
Survival prediction, utilizing pathological images and genomic profiles, is increasingly important in cancer analysis and prognosis. Despite significant progress, precise survival analysis still faces two main challenges…
PrognosisSurvival AnalysisSurvival Predictionwhole slide imagesMoME: Mixture of Multimodal Experts for Cancer Survival Prediction
Survival analysis, as a challenging task, requires integrating Whole Slide Images (WSIs) and genomic data for comprehensive decision-making. There are two main challenges in this task: significant heterogeneity and compl…
Survival AnalysisSurvival Predictionwhole slide imagesEmbedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes
Cancer clinics capture disease data at various scales, from genetic to organ level. Current bioinformatic methods struggle to handle the heterogeneous nature of this data, especially with missing modalities. We propose P…
Data IntegrationGraph Neural NetworkSurvival AnalysisSurvival PredictionPulmonary Embolism Mortality Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data
Purpose: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using Computed Tomography Pulmonary Angiography (CTPA), cli…
Mortality PredictionSurvival PredictionmTREE: Multi-Level Text-Guided Representation End-to-End Learning for Whole Slide Image Analysis
Multi-modal learning adeptly integrates visual and textual data, but its application to histopathology image and text analysis remains challenging, particularly with large, high-resolution images like gigapixel Whole Sli…
Survival Predictionwhole slide images