Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Evaluating Deep Learning Models for Breast Cancer Classification: A Comparative Study
This study evaluates the effectiveness of deep learning models in classifying histopathological images for early and accurate detection of breast cancer. Eight advanced models, including ResNet-50, DenseNet-121, ResNeXt-…
Breast Cancer DetectionCancer ClassificationTransfer LearningExploring Machine Learning Models for Lung Cancer Level Classification: A comparative ML Approach
This paper explores machine learning (ML) models for classifying lung cancer levels to improve diagnostic accuracy and prognosis. Through parameter tuning and rigorous evaluation, we assess various ML algorithms. Techniq…
Cancer ClassificationDiagnosticPrognosisInterpretable breast cancer classification using CNNs on mammographic images
Deep learning models have achieved promising results in breast cancer classification, yet their 'black-box' nature raises interpretability concerns. This research addresses the crucial need to gain insights into the deci…
Cancer ClassificationComputational EfficiencyDecision MakingAdvances in Multiple Instance Learning for Whole Slide Image Analysis: Techniques, Challenges, and Future Directions
Whole slide images (WSIs) are gigapixel-scale digital images of H\&E-stained tissue samples widely used in pathology. The substantial size and complexity of WSIs pose unique analytical challenges. Multiple Instance Learn…
Cancer ClassificationInterpretable Machine LearningMultiple Instance LearningSurvey+1Improving Lung Cancer Diagnosis and Survival Prediction with Deep Learning and CT Imaging
Lung cancer is a major cause of cancer-related deaths, and early diagnosis and treatment are crucial for improving patients' survival outcomes. In this paper, we propose to employ convolutional neural networks to model t…
Cancer ClassificationLung Cancer DiagnosisSurvival PredictionHistoGym: A Reinforcement Learning Environment for Histopathological Image Analysis
In pathological research, education, and clinical practice, the decision-making process based on pathological images is critically important. This significance extends to digital pathology image analysis: its adequacy is…
Cancer ClassificationOpenAI Gymreinforcement-learningReinforcement Learning+1HyperMM : Robust Multimodal Learning with Varying-sized Inputs
Combining multiple modalities carrying complementary information through multimodal learning (MML) has shown considerable benefits for diagnosing multiple pathologies. However, the robustness of multimodal models to miss…
Alzheimer's Disease DetectionCancer ClassificationImputationEnhancing the Utility of Privacy-Preserving Cancer Classification using Synthetic Data
Deep learning holds immense promise for aiding radiologists in breast cancer detection. However, achieving optimal model performance is hampered by limitations in availability and sharing of data commonly associated to p…
Breast Cancer DetectionCancer ClassificationData AugmentationDeep Learning+4Stacking-Enhanced Bagging Ensemble Learning for Breast Cancer Classification with CNN
This paper proposes a CNN classification network based on Bagging and stacking ensemble learning methods for breast cancer classification. The model was trained and tested on the public dataset of DDSM. The model is capa…
Binary ClassificationCancer ClassificationClassificationEnsemble LearningPan-cancer Histopathology WSI Pre-training with Position-aware Masked Autoencoder
Large-scale pre-training models have promoted the development of histopathology image analysis. However, existing self-supervised methods for histopathology images primarily focus on learning patch features, while there …
Cancer ClassificationPositionRepresentation LearningSelf-Supervised LearningDIOR-ViT: Differential Ordinal Learning Vision Transformer for Cancer Classification in Pathology Images
In computational pathology, cancer grading has been mainly studied as a categorical classification problem, which does not utilize the ordering nature of cancer grades such as the higher the grade is, the worse the cance…
Cancer ClassificationClassificationOrdinal ClassificationDeep learning for automated detection of breast cancer in deep ultraviolet fluorescence images with diffusion probabilistic model
Data limitation is a significant challenge in applying deep learning to medical images. Recently, the diffusion probabilistic model (DPM) has shown the potential to generate high-quality images by converting Gaussian ran…
Breast Cancer DetectionCancer ClassificationLearning a Clinically-Relevant Concept Bottleneck for Lesion Detection in Breast Ultrasound
Detecting and classifying lesions in breast ultrasound images is a promising application of artificial intelligence (AI) for reducing the burden of cancer in regions with limited access to mammography. Such AI systems ar…
Cancer ClassificationLesion DetectionUnsupervised Latent Stain Adaptation for Computational Pathology
In computational pathology, deep learning (DL) models for tasks such as segmentation or tissue classification are known to suffer from domain shifts due to different staining techniques. Stain adaptation aims to reduce t…
Cancer Classificationwhole slide imagesLeveraging Knowledge Distillation for Lightweight Skin Cancer Classification: Balancing Accuracy and Computational Efficiency
Skin cancer is a major concern to public health, accounting for one-third of the reported cancers. If not detected early, the cancer has the potential for severe consequences. Recognizing the critical need for effective …
Cancer ClassificationComputational EfficiencyData AugmentationKnowledge Distillation+2A Wavelet Guided Attention Module for Skin Cancer Classification with Gradient-based Feature Fusion
Skin cancer is a highly dangerous type of cancer that requires an accurate diagnosis from experienced physicians. To help physicians diagnose skin cancer more efficiently, a computer-aided diagnosis (CAD) system can be v…
Cancer ClassificationSkin Cancer ClassificationTowards Multi-modality Fusion and Prototype-based Feature Refinement for Clinically Significant Prostate Cancer Classification in Transrectal Ultrasound
Prostate cancer is a highly prevalent cancer and ranks as the second leading cause of cancer-related deaths in men globally. Recently, the utilization of multi-modality transrectal ultrasound (TRUS) has gained significan…
Cancer ClassificationBiomarker based Cancer Classification using an Ensemble with Pre-trained Models
Certain cancer types, namely pancreatic cancer is difficult to detect at an early stage; sparking the importance of discovering the causal relationship between biomarkers and cancer to identify cancer efficiently. By all…
Binary ClassificationCancer ClassificationMulti-class ClassificationMitigating annotation shift in cancer classification using single image generative models
Artificial Intelligence (AI) has emerged as a valuable tool for assisting radiologists in breast cancer detection and diagnosis. However, the success of AI applications in this domain is restricted by the quantity and qu…
Breast Cancer DetectionCancer ClassificationData AugmentationAI in Lung Health: Benchmarking Detection and Diagnostic Models Across Multiple CT Scan Datasets
Lung cancer's high mortality rate can be mitigated by early detection, increasingly reliant on AI for diagnostic imaging. However, AI model performance depends on training and validation datasets. This study develops and…
BenchmarkingCancer ClassificationDiagnosticLung Cancer Diagnosis+1