Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
TSEML: A task-specific embedding-based method for few-shot classification of cancer molecular subtypes
Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data sam…
Cancer ClassificationMeta-LearningReliable Breast Cancer Molecular Subtype Prediction based on uncertainty-aware Bayesian Deep Learning by Mammography
Breast cancer is a heterogeneous disease with different molecular subtypes, clinical behavior, treatment responses as well as survival outcomes. The development of a reliable, accurate, available and inexpensive method t…
Cancer ClassificationClassificationDeep LearningPrognosis+1Breast Tumor Classification Using EfficientNet Deep Learning Model
Precise breast cancer classification on histopathological images has the potential to greatly improve the diagnosis and patient outcome in oncology. The data imbalance problem largely stems from the inherent imbalance wi…
Binary ClassificationCancer ClassificationClassificationData Augmentation+4Refining Focus in AI for Lung Cancer: Comparing Lesion-Centric and Chest-Region Models with Performance Insights from Internal and External Validation
Background: AI-based classification models are essential for improving lung cancer diagnosis. However, the relative performance of lesion-level versus chest-region models in internal and external datasets remains unclear…
Cancer ClassificationLung Cancer DiagnosisPeritumoral Expansion Radiomics for Improved Lung Cancer Classification
Purpose: This study investigated how nodule segmentation and surrounding peritumoral regions influence radionics-based lung cancer classification. Methods: Using 3D CT scans with bounding box annotated nodules, we genera…
3D ClassificationCancer ClassificationClassificationDiagnostic+2Are nuclear masks all you need for improved out-of-domain generalisation? A closer look at cancer classification in histopathology
Domain generalisation in computational histopathology is challenging because the images are substantially affected by differences among hospitals due to factors like fixation and staining of tissue and imaging equipment.…
AllCancer ClassificationData AugmentationNuclear SegmentationCancer-Net SCa-Synth: An Open Access Synthetically Generated 2D Skin Lesion Dataset for Skin Cancer Classification
In the United States, skin cancer ranks as the most commonly diagnosed cancer, presenting a significant public health issue due to its high rates of occurrence and the risk of serious complications if not caught early. R…
Cancer ClassificationSkin Cancer ClassificationMamT$^4$: Multi-view Attention Networks for Mammography Cancer Classification
In this study, we introduce a novel method, called MamT$^4$, which is used for simultaneous analysis of four mammography images. A decision is made based on one image of a breast, with attention also devoted to three add…
Cancer ClassificationDCT-HistoTransformer: Efficient Lightweight Vision Transformer with DCT Integration for histopathological image analysis
In recent years, the integration of advanced imaging techniques and deep learning methods has significantly advanced computer-aided diagnosis (CAD) systems for breast cancer detection and classification. Transformers, wh…
Binary ClassificationBreast Cancer DetectionCancer ClassificationClassification+2New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture
The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, incl…
Cancer Classificationimage-classificationImage ClassificationTransfer LearningPND-Net: Plant Nutrition Deficiency and Disease Classification using Graph Convolutional Network
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior perfor…
Cancer ClassificationClassificationimage-classificationImage Classification+1Performance Evaluation of Deep Learning and Transformer Models Using Multimodal Data for Breast Cancer Classification
Rising breast cancer (BC) occurrence and mortality are major global concerns for women. Deep learning (DL) has demonstrated superior diagnostic performance in BC classification compared to human expert readers. However, …
Cancer ClassificationDiagnosticPrecision Cancer Classification and Biomarker Identification from mRNA Gene Expression via Dimensionality Reduction and Explainable AI
Gene expression analysis is a critical method for cancer classification, enabling precise diagnoses through the identification of unique molecular signatures associated with various tumors. Identifying cancer-specific ge…
Cancer ClassificationDimensionality Reductionfeature selectionMECFormer: Multi-task Whole Slide Image Classification with Expert Consultation Network
Whole slide image (WSI) classification is a crucial problem for cancer diagnostics in clinics and hospitals. A WSI, acquired at gigapixel size, is commonly tiled into patches and processed by multiple-instance learning (…
Cancer ClassificationClassificationDecoderimage-classification+2Comparative Analysis of Multi-Omics Integration Using Advanced Graph Neural Networks for Cancer Classification
Multi-omics data is increasingly being utilized to advance computational methods for cancer classification. However, multi-omics data integration poses significant challenges due to the high dimensionality, data complexi…
Cancer ClassificationData Integrationfeature selectionGraph Attention+1Comparative Analysis and Ensemble Enhancement of Leading CNN Architectures for Breast Cancer Classification
This study introduces a novel and accurate approach to breast cancer classification using histopathology images. It systematically compares leading Convolutional Neural Network (CNN) models across varying image datasets,…
Cancer ClassificationClassificationData AugmentationQuantifying Cancer Likeness: A Statistical Approach for Pathological Image Diagnosis
In this paper, we present a new statistical approach to automatically identify cancer regions in pathological images. The proposed method is built from statistical theory in line with evidence-based medicine. The two cor…
Cancer ClassificationClassificationMulti-modal Medical Image Fusion For Non-Small Cell Lung Cancer Classification
The early detection and nuanced subtype classification of non-small cell lung cancer (NSCLC), a predominant cause of cancer mortality worldwide, is a critical and complex issue. In this paper, we introduce an innovative …
Cancer ClassificationSelectiveKD: A semi-supervised framework for cancer detection in DBT through Knowledge Distillation and Pseudo-labeling
When developing Computer Aided Detection (CAD) systems for Digital Breast Tomosynthesis (DBT), the complexity arising from the volumetric nature of the modality poses significant technical challenges for obtaining large-…
Cancer ClassificationKnowledge DistillationLASSO-MOGAT: A Multi-Omics Graph Attention Framework for Cancer Classification
The application of machine learning methods to analyze changes in gene expression patterns has recently emerged as a powerful approach in cancer research, enhancing our understanding of the molecular mechanisms underpinn…
Cancer Classificationfeature selectionGraph Attention