Papers Tumour Classification
“Tumour Classification” 태그가 달린 논문 17편 · 필터 해제
LadderMIL: Multiple Instance Learning with Coarse-to-Fine Self-Distillation
Multiple Instance Learning (MIL) for whole slide image (WSI) analysis in computational pathology often neglects instance-level learning as supervision is typically provided only at the bag level. In this work, we present…
BenchmarkingClassificationMultiple Instance LearningPrognosis+1Brain tumour classification using BoF-SURF with filter-based feature selection methods
Currently, cancer is a global concern with a focus on reducing its incidence and advancing diagnostic techniques. Faster and more precise cancer cell detection improves treatment and survival prospects. The objective of …
Cell DetectionDiagnosticfeature selectionTumour ClassificationMedISure: Towards Assuring Machine Learning-based Medical Image Classifiers using Mixup Boundary Analysis
Machine learning (ML) models are becoming integral in healthcare technologies, presenting a critical need for formal assurance to validate their safety, fairness, robustness, and trustworthiness. These models are inheren…
Cancer ClassificationFairnessTumour ClassificationDeep Learning Approaches to Osteosarcoma Diagnosis and Classification: A Comparative Methodological Approach
Background: Osteosarcoma is the most common primary malignancy of the bone, being most prevalent in childhood and adolescence. Despite recent progress in diagnostic methods, histopathology remains the gold standard for d…
DiagnosticTumour ClassificationGenetic Analysis of Prostate Cancer with Computer Science Methods
Metastatic prostate cancer is one of the most common cancers in men. In the advanced stages of prostate cancer, tumours can metastasise to other tissues in the body, which is fatal. In this thesis, we performed a genetic…
Community DetectionTumour ClassificationComplex Network for Complex Problems: A comparative study of CNN and Complex-valued CNN
Neural networks, especially convolutional neural networks (CNN), are one of the most common tools these days used in computer vision. Most of these networks work with real-valued data using real-valued features. Complex-…
Tumour ClassificationWeakly-supervised segmentation using inherently-explainable classification models and their application to brain tumour classification
Deep learning models have shown their potential for several applications. However, most of the models are opaque and difficult to trust due to their complex reasoning - commonly known as the black-box problem. Some field…
ClassificationDecision MakingSegmentationTumour Classification+1An End-to-End Breast Tumour Classification Model Using Context-Based Patch Modelling- A BiLSTM Approach for Image Classification
Researchers working on computational analysis of Whole Slide Images (WSIs) in histopathology have primarily resorted to patch-based modelling due to large resolution of each WSI. The large resolution makes WSIs infeasibl…
Breast Tumour ClassificationClassificationimage-classificationImage Classification+2Classification of Brain Tumours in MR Images using Deep Spatiospatial Models
A brain tumour is a mass or cluster of abnormal cells in the brain, which has the possibility of becoming life-threatening because of its ability to invade neighbouring tissues and also form metastases. An accurate diagn…
DiagnosticTumour ClassificationXOmiVAE: an interpretable deep learning model for cancer classification using high-dimensional omics data
The lack of explainability is one of the most prominent disadvantages of deep learning applications in omics. This "black box" problem can undermine the credibility and limit the practical implementation of biomedical de…
Cancer ClassificationClassificationClusteringDeep Learning+1Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images
Histology images are inherently symmetric under rotation, where each orientation is equally as likely to appear. However, this rotational symmetry is not widely utilised as prior knowledge in modern Convolutional Neural …
Breast Tumour ClassificationColorectal Gland Segmentation:Multi-tissue Nucleus SegmentationNuclear Segmentation+1Integrated Multi-omics Analysis Using Variational Autoencoders: Application to Pan-cancer Classification
Different aspects of a clinical sample can be revealed by multiple types of omics data. Integrated analysis of multi-omics data provides a comprehensive view of patients, which has the potential to facilitate more accura…
Cancer ClassificationClassificationDecision MakingGeneral Classification+2An ensemble of machine learning and anti-learning methods for predicting tumour patient survival rates
This paper primarily addresses a dataset relating to cellular, chemical and physical conditions of patients gathered at the time they are operated upon to remove colorectal tumours. This data provides a unique insight in…
BIG-bench Machine Learningfeature selectionGeneral ClassificationPrognosis+1Ensemble Learning of Colorectal Cancer Survival Rates
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point…
ClusteringEnsemble LearningGeneral ClassificationPrognosis+2Iterative Multilevel MRF Leveraging Context and Voxel Information for Brain Tumour Segmentation in MRI
In this paper, we introduce a fully automated multistage graphical probabilistic framework to segment brain tumours from multimodal Magnetic Resonance Images (MRIs) acquired from real patients. An initial Bayesian tumour…
SegmentationTumour ClassificationBiomarker Clustering of Colorectal Cancer Data to Complement Clinical Classification
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point…
ClassificationClusteringGeneral ClassificationTumour ClassificationSupervised Learning and Anti-learning of Colorectal Cancer Classes and Survival Rates from Cellular Biology Parameters
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point…
Tumour Classification