Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Standardizing Medical Images at Scale for AI
Deep learning has achieved remarkable success in medical image analysis, yet its performance remains highly sensitive to the heterogeneity of clinical data. Differences in imaging hardware, staining protocols, and acquis…
Cancer ClassificationDeepHistoViT: An Interpretable Vision Transformer Framework for Histopathological Cancer Classification
Histopathology remains the gold standard for cancer diagnosis because it provides detailed cellular-level assessment of tissue morphology. However, manual histopathological examination is time-consuming, labour-intensive…
Cancer ClassificationLayout-Guided Controllable Pathology Image Generation with In-Context Diffusion Transformers
Controllable pathology image synthesis requires reliable regulation of spatial layout, tissue morphology, and semantic detail. However, existing text-guided diffusion models offer only coarse global control and lack the …
Cancer ClassificationData AugmentationImage GenerationClass Visualizations and Activation Atlases for Enhancing Interpretability in Deep Learning-Based Computational Pathology
The rapid adoption of transformer-based models in computational pathology has enabled prediction of molecular and clinical biomarkers from H&E whole-slide images, yet interpretability has not kept pace with model complex…
Cancer ClassificationDeep Modeling and Interpretation for Bladder Cancer Classification
Deep models based on vision transformer (ViT) and convolutional neural network (CNN) have demonstrated remarkable performance on natural datasets. However, these models may not be similar in medical imaging, where abnorm…
Cancer ClassificationA Geometric Multimodal Foundation Model Integrating Bp-MRI and Clinical Reports in Prostate Cancer Classification
Prostate cancer (PCa) is one of the most common cancers in men worldwide. Bi-parametric MRI (bp-MRI) and clinical variables are crucial for PCa identification and improving treatment decisions. However, this process is s…
Cancer ClassificationLost in Translation: How Language Re-Aligns Vision for Cross-Species Pathology
Foundation models are increasingly applied to computational pathology, yet their behavior under cross-cancer and cross-species transfer remains unspecified. This study investigated how fine-tuning CPath-CLIP affects canc…
Cancer ClassificationAn Innovative Framework for Breast Cancer Detection Using Pyramid Adaptive Atrous Convolution, Transformer Integration, and Multi-Scale Feature Fusion
Breast cancer is one of the most common cancers among women worldwide, and its accurate and timely diagnosis plays a critical role in improving treatment outcomes. This thesis presents an innovative framework for detecti…
Breast Cancer DetectionCancer ClassificationData AugmentationSelf-learned representation-guided latent diffusion model for breast cancer classification in deep ultraviolet whole surface images
Breast-Conserving Surgery (BCS) requires precise intraoperative margin assessment to preserve healthy tissue. Deep Ultraviolet Fluorescence Scanning Microscopy (DUV-FSM) offers rapid, high-resolution surface imaging for …
Self-Supervised LearningCancer ClassificationHierarchical Pooling and Explainability in Graph Neural Networks for Tumor and Tissue-of-Origin Classification Using RNA-seq Data
This study explores the use of graph neural networks (GNNs) with hierarchical pooling and multiple convolution layers for cancer classification based on RNA-seq data. We combine gene expression data from The Cancer Genom…
Dimensionality ReductionCancer ClassificationBeyond Occlusion: In Search for Near Real-Time Explainability of CNN-Based Prostate Cancer Classification
Deep neural networks are starting to show their worth in critical applications such as assisted cancer diagnosis. However, for their outputs to get accepted in practice, the results they provide should be explainable in …
Cancer ClassificationA Hybrid Deep Learning Framework with Explainable AI for Lung Cancer Classification with DenseNet169 and SVM
Lung cancer is a very deadly disease worldwide, and its early diagnosis is crucial for increasing patient survival rates. Computed tomography (CT) scans are widely used for lung cancer diagnosis as they can give detailed…
Cancer ClassificationLung Cancer DiagnosisResource-Efficient Variational Quantum Classifier
We introduce the unambiguous quantum classifier based on Hamming distance measurements combined with classical post-processing. The proposed approach improves classification performance through a more effective use of an…
Cancer ClassificationMeisenMeister: A Simple Two Stage Pipeline for Breast Cancer Classification on MRI
The ODELIA Breast MRI Challenge 2025 addresses a critical issue in breast cancer screening: improving early detection through more efficient and accurate interpretation of breast MRI scans. Even though methods for genera…
Breast Cancer DetectionCancer ClassificationLesion SegmentationMV-MLM: Bridging Multi-View Mammography and Language for Breast Cancer Diagnosis and Risk Prediction
Large annotated datasets are essential for training robust Computer-Aided Diagnosis (CAD) models for breast cancer detection or risk prediction. However, acquiring such datasets with fine-detailed annotation is both cost…
Breast Cancer DetectionCancer ClassificationUSF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencoding
Ultrasound imaging is one of the most widely used diagnostic modalities, offering real-time, radiation-free assessment across diverse clinical domains. However, interpretation of ultrasound images remains challenging due…
Cancer ClassificationHeFS: Helper-Enhanced Feature Selection via Pareto-Optimized Genetic Search
Feature selection is a combinatorial optimization problem that is NP-hard. Conventional approaches often employ heuristic or greedy strategies, which are prone to premature convergence and may fail to capture subtle yet …
Cancer ClassificationLung Cancer Classification from CT Images Using ResNet
Lung cancer, a malignancy originating in lung tissues, is commonly diagnosed and classified using medical imaging techniques, particularly computed tomography (CT). Despite the integration of machine learning and deep le…
Multi-class ClassificationCancer ClassificationBinary ClassificationFrom Generic to Specialized: A Subspecialty Diagnostic System Powered by Self-Supervised Learning for Cervical Histopathology
Cervical cancer remains a major malignancy, necessitating extensive and complex histopathological assessments and comprehensive support tools. Although deep learning shows promise, these models still lack accuracy and ge…
Self-Supervised LearningCancer ClassificationDeepAf: One-Shot Spatiospectral Auto-Focus Model for Digital Pathology
While Whole Slide Imaging (WSI) scanners remain the gold standard for digitizing pathology samples, their high cost limits accessibility in many healthcare settings. Other low-cost solutions also face critical limitation…
Cancer Classification