Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Breast Cancer Classification using Deep Learned Features Boosted with Handcrafted Features
Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and impro…
Cancer ClassificationGene selection from microarray expression data: A Multi-objective PSO with adaptive K-nearest neighborhood
Cancer detection is one of the key research topics in the medical field. Accurate detection of different cancer types is valuable in providing better treatment facilities and risk minimization for patients. This paper de…
Cancer ClassificationClassificationfeature selectionEvaluation of Multi-Scale Multiple Instance Learning to Improve Thyroid Cancer Classification
Thyroid cancer is currently the fifth most common malignancy diagnosed in women. Since differentiation of cancer sub-types is important for treatment and current, manual methods are time consuming and subjective, automat…
Cancer ClassificationMultiple Instance Learningwhole slide imagesEnhancing Non-mass Breast Ultrasound Cancer Classification With Knowledge Transfer
Much progress has been made in the deep neural network (DNN) based diagnosis of mass lesions breast ultrasound (BUS) images. However, the non-mass lesion is less investigated because of the limited data. Based on the ins…
Cancer ClassificationClassificationTransfer LearningMulti-View Hypercomplex Learning for Breast Cancer Screening
Traditionally, deep learning methods for breast cancer classification perform a single-view analysis. However, radiologists simultaneously analyze all four views that compose a mammography exam, owing to the correlations…
Breast Tumour ClassificationCancer ClassificationCancer-no cancer per breast classificationClassification+2From Modern CNNs to Vision Transformers: Assessing the Performance, Robustness, and Classification Strategies of Deep Learning Models in Histopathology
While machine learning is currently transforming the field of histopathology, the domain lacks a comprehensive evaluation of state-of-the-art models based on essential but complementary quality requirements beyond a mere…
BenchmarkingCancer ClassificationClassificationImage-to-Image Translation+1New pyramidal hybrid textural and deep features based automatic skin cancer classification model: Ensemble DarkNet and textural feature extractor
Background: Skin cancer is one of the widely seen cancer worldwide and automatic classification of skin cancer can be benefited dermatology clinics for an accurate diagnosis. Hence, a machine learning-based automatic ski…
Cancer ClassificationQuantizationSkin Cancer ClassificationAn Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks
Cancer classification based on gene expression increases early diagnosis and recovery, but high-dimensional genes with a small number of samples are a major challenge. This work introduces a new hybrid quantum kernel sup…
Cancer Classificationfeature selectionAnalyzing 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 classificationLearning with Less Labels in Digital Pathology via Scribble Supervision from Natural Images
A critical challenge of training deep learning models in the Digital Pathology (DP) domain is the high annotation cost by medical experts. One way to tackle this issue is via transfer learning from the natural image doma…
Cancer ClassificationSegmentationTransfer LearningBiomarker Gene Identification for Breast Cancer Classification
BACKGROUND: Breast cancer has emerged as one of the most prevalent cancers among women leading to a high mortality rate. Due to the heterogeneous nature of breast cancer, there is a need to identify differentially expres…
Cancer ClassificationClassificationBreast Cancer Classification Using: Pixel Interpolation
Image Processing represents the backbone research area within engineering and computer science specialization. It is promptly growing technologies today, and its applications founded in various aspects of biomedical fiel…
Cancer ClassificationClassificationSkin Cancer Classification using Inception Network and Transfer Learning
Medical data classification is typically a challenging task due to imbalance between classes. In this paper, we propose an approach to classify dermatoscopic images from HAM10000 (Human Against Machine with 10000 trainin…
Cancer ClassificationClassificationSkin Cancer ClassificationTransfer LearningBenchmarking of Lightweight Deep Learning Architectures for Skin Cancer Classification using ISIC 2017 Dataset
Skin cancer is one of the deadly types of cancer and is common in the world. Recently, there has been a huge jump in the rate of people getting skin cancer. For this reason, the number of studies on skin cancer classific…
BenchmarkingCancer ClassificationDeep LearningSkin Cancer Classification+1Memory-aware curriculum federated learning for breast cancer classification
For early breast cancer detection, regular screening with mammography imaging is recommended. Routinary examinations result in datasets with a predominant amount of negative samples. A potential solution to such class-im…
Breast Cancer DetectionCancer ClassificationClassificationDomain Adaptation+2Deep Learning for Breast Cancer Classification: Enhanced Tangent Function
Background and Aim: Recently, deep learning using convolutional neural network has been used successfully to classify the images of breast cells accurately. However, the accuracy of manual classification of those histopa…
Cancer ClassificationClassificationDeep LearningHybrid gene selection approach using XGBoost and multi-objective genetic algorithm for cancer classification
Microarray gene expression data are often accompanied by a large number of genes and a small number of samples. However, only a few of these genes are relevant to cancer, resulting in signigicant gene selection challenge…
Cancer Classificationfeature selectionXOmiVAE: 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+1Soft-Attention Improves Skin Cancer Classification Performance
In clinical applications, neural networks must focus on and highlight the most important parts of an input image. Soft-Attention mechanism enables a neural network toachieve this goal. This paper investigates the effecti…
Cancer ClassificationClassificationGeneral ClassificationImage Classification+2A Smartphone based Application for Skin Cancer Classification Using Deep Learning with Clinical Images and Lesion Information
Over the last decades, the incidence of skin cancer, melanoma and non-melanoma, has increased at a continuous rate. In particular for melanoma, the deadliest type of skin cancer, early detection is important to increase …
Cancer ClassificationPrognosisSkin Cancer Classification