Papers Cancer type classification
“Cancer type classification” 태그가 달린 논문 8편 · 필터 해제
Current Pathology Foundation Models are unrobust to Medical Center Differences
Pathology Foundation Models (FMs) hold great promise for healthcare. Before they can be used in clinical practice, it is essential to ensure they are robust to variations between medical centers. We measure whether patho…
Cancer type classificationUnsupervised Cognition
Unsupervised learning methods have a soft inspiration in cognition models. To this day, the most successful unsupervised learning methods revolve around clustering samples in a mathematical space. In this paper we propos…
Cancer type classificationDecision MakingPitfalls of Conditional Batch Normalization for Contextual Multi-Modal Learning
Humans have perfected the art of learning from multiple modalities through sensory organs. Despite their impressive predictive performance on a single modality, neural networks cannot reach human level accuracy with resp…
Cancer type classificationSelf-omics: A Self-supervised Learning Framework for Multi-omics Cancer Data
We have gained access to vast amounts of multi-omics data thanks to Next Generation Sequencing. However, it is challenging to analyse this data due to its high dimensionality and much of it not being annotated. Lack of a…
Cancer type classificationSelf-Supervised Learningzero-shot-classificationZero-Shot LearningAnalyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification
Ribonucleic acid Sequencing (RNA-Seq) analysis is particularly useful for obtaining insights into differentially expressed genes. However, it is challenging because of its high-dimensional data. Such analysis is a tool w…
Cancer ClassificationCancer type classificationSubOmiEmbed: Self-supervised Representation Learning of Multi-omics Data for Cancer Type Classification
For personalized medicines, very crucial intrinsic information is present in high dimensional omics data which is difficult to capture due to the large number of molecular features and small number of available samples. …
Cancer type classificationDecision MakingRepresentation LearningSelf-Supervised LearningDeep Discriminative Fine-Tuning for Cancer Type Classification
Determining the primary site of origin for metastatic tumors is one of the open problems in cancer care because the efficacy of treatment often depends on the cancer tissue of origin. Classification methods that can leve…
Cancer type classificationClassificationGeneral ClassificationTransfer Learning+1Representation Transfer for Differentially Private Drug Sensitivity Prediction
Motivation: Human genomic datasets often contain sensitive information that limits use and sharing of the data. In particular, simple anonymisation strategies fail to provide sufficient level of protection for genomic da…
BIG-bench Machine LearningCancer type classificationDimensionality ReductionPrediction+2