Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Fuzzy Gene Selection and Cancer Classification Based on Deep Learning Model
Machine learning (ML) approaches have been used to develop highly accurate and efficient applications in many fields including bio-medical science. However, even with advanced ML techniques, cancer classification using g…
Cancer ClassificationDeep Learningfeature selectionT Cell Receptor Protein Sequences and Sparse Coding: A Novel Approach to Cancer Classification
Cancer is a complex disease characterized by uncontrolled cell growth and proliferation. T cell receptors (TCRs) are essential proteins for the adaptive immune system, and their specific recognition of antigens plays a c…
Cancer ClassificationMulti-class ClassificationSpecificityDomain shifts in dermoscopic skin cancer datasets: Evaluation of essential limitations for clinical translation
The limited ability of Convolutional Neural Networks to generalize to images from previously unseen domains is a major limitation, in particular, for safety-critical clinical tasks such as dermoscopic skin cancer classif…
Cancer ClassificationDomain AdaptationSkin Cancer ClassificationUnsupervised Domain AdaptationCerviFormer: A Pap-smear based cervical cancer classification method using cross attention and latent transformer
Purpose: Cervical cancer is one of the primary causes of death in women. It should be diagnosed early and treated according to the best medical advice, as with other diseases, to ensure that its effects are as minimal as…
Cancer ClassificationClassificationMulti-class Skin Cancer Classification Architecture Based on Deep Convolutional Neural Network
Skin cancer detection is challenging since different types of skin lesions share high similarities. This paper proposes a computer-based deep learning approach that will accurately identify different kinds of skin lesion…
Cancer ClassificationData AugmentationDeep LearningSkin Cancer Classification+1Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning
In many histopathology tasks, sample classification depends on morphological details in tissue or single cells that are only visible at the highest magnification. For a pathologist, this implies tedious zooming in and ou…
Active LearningCancer ClassificationMultiple Instance Learningwhole slide imagesA Multimodal Graph Neural Network Framework of Cancer Molecular Subtype Classification
The recent development of high-throughput sequencing creates a large collection of multi-omics data, which enables researchers to better investigate cancer molecular profiles and cancer taxonomy based on molecular subtyp…
Cancer ClassificationClassificationGraph AttentionGraph Neural NetworkMachine Learning Methods for Cancer Classification Using Gene Expression Data: A Review
Cancer is a term that denotes a group of diseases caused by abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of d…
Cancer ClassificationFeature EngineeringAdversarial Attacks and Defences for Skin Cancer Classification
There has been a concurrent significant improvement in the medical images used to facilitate diagnosis and the performance of machine learning techniques to perform tasks such as classification, detection, and segmentati…
Adversarial AttackAdversarial DefenseCancer ClassificationClassification+5Mixed Supervision of Histopathology Improves Prostate Cancer Classification from MRI
Non-invasive prostate cancer detection from MRI has the potential to revolutionize patient care by providing early detection of clinically-significant disease (ISUP grade group >= 2), but has thus far shown limited posit…
Cancer ClassificationSiamese Neural Networks for Skin Cancer Classification and New Class Detection using Clinical and Dermoscopic Image Datasets
Skin cancer is the most common malignancy in the world. Automated skin cancer detection would significantly improve early detection rates and prevent deaths. To help with this aim, a number of datasets have been released…
Cancer ClassificationSkin Cancer ClassificationGenerating and Weighting Semantically Consistent Sample Pairs for Ultrasound Contrastive Learning
Well-annotated medical datasets enable deep neural networks (DNNs) to gain strong power in extracting lesion-related features. Building such large and well-designed medical datasets is costly due to the need for high-lev…
Cancer ClassificationContrastive LearningMeta-LearningPneumonia Detection+1Reversing Skin Cancer Adversarial Examples by Multiscale Diffusive and Denoising Aggregation Mechanism
Reliable skin cancer diagnosis models play an essential role in early screening and medical intervention. Prevailing computer-aided skin cancer classification systems employ deep learning approaches. However, recent stud…
Cancer ClassificationDenoisingSkin Cancer ClassificationDeep Learning Neural Network for Lung Cancer Classification: Enhanced Optimization Function
Background and Purpose: Convolutional neural network is widely used for image recognition in the medical area at nowadays. However, overall accuracy in predicting lung tumor is low and the processing time is high as the …
Cancer ClassificationImage ReconstructionBreast Cancer Classification Based on Histopathological Images Using a Deep Learning Capsule Network
Breast cancer is one of the most serious types of cancer that can occur in women. The automatic diagnosis of breast cancer by analyzing histological images (HIs) is important for patients and their prognosis. The classif…
Cancer ClassificationPrognosisComparison of Deep Learning and Machine Learning Models and Frameworks for Skin Lesion Classification
The incidence rate for skin cancer has been steadily increasing throughout the world, leading to it being a serious issue. Diagnosis at an early stage has the potential to drastically reduce the harm caused by the diseas…
Cancer ClassificationLesion ClassificationLesion DetectionSkin Cancer Classification+1Lesion detection in contrast enhanced spectral mammography
Background \& purpose: The recent emergence of neural networks models for the analysis of breast images has been a breakthrough in computer aided diagnostic. This approach was not yet developed in Contrast Enhanced Spect…
Cancer ClassificationDiagnosticLesion DetectionManagementImproving the diagnosis of breast cancer based on biophysical ultrasound features utilizing machine learning
The improved diagnostic accuracy of ultrasound breast examinations remains an important goal. In this study, we propose a biophysical feature based machine learning method for breast cancer detection to improve the perfo…
Breast Cancer DetectionCancer ClassificationDiagnosticEfficient Lung Cancer Image Classification and Segmentation Algorithm Based on Improved Swin Transformer
With the development of computer technology, various models have emerged in artificial intelligence. The transformer model has been applied to the field of computer vision (CV) after its success in natural language proce…
Cancer ClassificationDiagnosticimage-classificationImage Classification+2Personalized Diagnostic Tool for Thyroid Cancer Classification using Multi-view Ultrasound
Over the past decades, the incidence of thyroid cancer has been increasing globally. Accurate and early diagnosis allows timely treatment and helps to avoid over-diagnosis. Clinically, a nodule is commonly evaluated from…
Cancer ClassificationDecision MakingDiagnostic