Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Theoretical Framework for Tempered Fractional Gradient Descent: Application to Breast Cancer Classification
This paper introduces Tempered Fractional Gradient Descent (TFGD), a novel optimization framework that synergizes fractional calculus with exponential tempering to enhance gradient-based learning. Traditional gradient de…
Cancer ClassificationMSAD-Net: Multiscale and Spatial Attention-based Dense Network for Lung Cancer Classification
Lung cancer, a severe form of malignant tumor that originates in the tissues of the lungs, can be fatal if not detected in its early stages. It ranks among the top causes of cancer-related mortality worldwide. Detecting …
Cancer ClassificationCytoFM: The first cytology foundation model
Cytology is essential for cancer diagnostics and screening due to its minimally invasive nature. However, the development of robust deep learning models for digital cytology is challenging due to the heterogeneity in sta…
Cancer ClassificationmodelMultiple Instance LearningPathOrchestra: A Comprehensive Foundation Model for Computational Pathology with Over 100 Diverse Clinical-Grade Tasks
The complexity and variability inherent in high-resolution pathological images present significant challenges in computational pathology. While pathology foundation models leveraging AI have catalyzed transformative adva…
Cancer ClassificationSelf-Supervised LearningGraph Kolmogorov-Arnold Networks for Multi-Cancer Classification and Biomarker Identification, An Interpretable Multi-Omics Approach
The integration of heterogeneous multi-omics datasets at a systems level remains a central challenge for developing analytical and computational models in precision cancer diagnostics. This paper introduces Multi-Omics G…
Cancer ClassificationDeep LearningKolmogorov-Arnold NetworksContrasting Low and High-Resolution Features for HER2 Scoring using Deep Learning
Breast cancer, the most common malignancy among women, requires precise detection and classification for effective treatment. Immunohistochemistry (IHC) biomarkers like HER2, ER, and PR are critical for identifying breas…
Cancer ClassificationClassificationPrognosisDiffusion models applied to skin and oral cancer classification
This study investigates the application of diffusion models in medical image classification (DiffMIC), focusing on skin and oral lesions. Utilizing the datasets PAD-UFES-20 for skin cancer and P-NDB-UFES for oral cancer,…
Binary ClassificationCancer ClassificationClassificationimage-classification+3Adaptive Deep Learning for Multiclass Breast Cancer Classification via Misprediction Risk Analysis
Breast cancer remains one of the leading causes of cancer-related deaths worldwide. Early detection is crucial for improving patient outcomes, yet the diagnostic process is often complex and prone to inconsistencies amon…
Binary ClassificationBreast Cancer DetectionCancer ClassificationDiagnosticRevisiting Invariant Learning for Out-of-Domain Generalization on Multi-Site Mammogram Datasets
Despite significant progress in robust deep learning techniques for mammogram breast cancer classification, their reliability in real-world clinical development settings remains uncertain. The translation of these models…
Cancer ClassificationDomain GeneralizationUSegMix: Unsupervised Segment Mix for Efficient Data Augmentation in Pathology Images
In computational pathology, researchers often face challenges due to the scarcity of labeled pathology datasets. Data augmentation emerges as a crucial technique to mitigate this limitation. In this study, we introduce a…
Cancer ClassificationData AugmentationSuperpixelsPatch Stitching Data Augmentation for Cancer Classification in Pathology Images
Computational pathology, integrating computational methods and digital imaging, has shown to be effective in advancing disease diagnosis and prognosis. In recent years, the development of machine learning and deep learni…
Cancer ClassificationData AugmentationPrognosisExploring Patient Data Requirements in Training Effective AI Models for MRI-based Breast Cancer Classification
The past decade has witnessed a substantial increase in the number of startups and companies offering AI-based solutions for clinical decision support in medical institutions. However, the critical nature of medical deci…
Breast Cancer DetectionCancer ClassificationTriad: Vision Foundation Model for 3D Magnetic Resonance Imaging
Vision foundation models (VFMs) are pre-trained on extensive image datasets to learn general representations for diverse types of data. These models can subsequently be fine-tuned for specific downstream tasks, significa…
Cancer ClassificationComputed Tomography (CT)Image RegistrationMedical Image Registration+1Joint Modelling Histology and Molecular Markers for Cancer Classification
Cancers are characterized by remarkable heterogeneity and diverse prognosis. Accurate cancer classification is essential for patient stratification and clinical decision-making. Although digital pathology has been advanc…
Cancer ClassificationPrognosiswhole slide imagesUsing Computer Vision for Skin Disease Diagnosis in Bangladesh Enhancing Interpretability and Transparency in Deep Learning Models for Skin Cancer Classification
With over 2 million new cases identified annually, skin cancer is the most prevalent type of cancer globally and the second most common in Bangladesh, following breast cancer. Early detection and treatment are crucial fo…
Cancer ClassificationDecision MakingDeep LearningSkin Cancer ClassificationMEDFORM: A Foundation Model for Contrastive Learning of CT Imaging and Clinical Numeric Data in Multi-Cancer Analysis
Computed tomography (CT) and clinical numeric data are essential modalities for cancer evaluation, but building large-scale multimodal training datasets for developing medical foundation models remains challenging due to…
Cancer ClassificationComputed Tomography (CT)Contrastive LearningFew-Shot Learning+3Comparative Analysis of Hand-Crafted and Machine-Driven Histopathological Features for Prostate Cancer Classification and Segmentation
Histopathological image analysis is a reliable method for prostate cancer identification. In this paper, we present a comparative analysis of two approaches for segmenting glandular structures in prostate images to autom…
Cancer ClassificationSegmentationSemantic SegmentationA Pan-cancer Classification Model using Multi-view Feature Selection Method and Ensemble Classifier
Accurately identifying cancer samples is crucial for precise diagnosis and effective patient treatment. Traditional methods falter with high-dimensional and high feature-to-sample count ratios, which are critical for cla…
Cancer Classificationfeature selectionMRANet: A Modified Residual Attention Networks for Lung and Colon Cancer Classification
Lung and colon cancers are predominant contributors to cancer mortality. Early and accurate diagnosis is crucial for effective treatment. By utilizing imaging technology in different image detection, learning models have…
Cancer ClassificationMiniGPT-Pancreas: Multimodal Large Language Model for Pancreas Cancer Classification and Detection
Problem: Pancreas radiological imaging is challenging due to the small size, blurred boundaries, and variability of shape and position of the organ among patients. Goal: In this work we present MiniGPT-Pancreas, a Multim…
Cancer ClassificationChatbotLanguage ModelingLanguage Modelling+3