Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Exploring Explainable AI Techniques for Improved Interpretability in Lung and Colon Cancer Classification
Lung and colon cancer are serious worldwide health challenges that require early and precise identification to reduce mortality risks. However, diagnosis, which is mostly dependent on histopathologists' competence, prese…
Cancer ClassificationDiagnosticimage-classificationImage ClassificationImproved and Explainable Cervical Cancer Classification using Ensemble Pooling of Block Fused Descriptors
Cervical cancer is the second most common cancer in women and causes high death rates. Earlier models for detecting cervical cancer had limited success. In this work, we propose new models that substantially outperform p…
Cancer Classificationfeature selectionimage-classificationImage ClassificationRad4XCNN: a new agnostic method for post-hoc global explanation of CNN-derived features by means of radiomics
In recent years, machine learning-based clinical decision support systems (CDSS) have played a key role in the analysis of several medical conditions. Despite their promising capabilities, the lack of transparency in AI …
Cancer ClassificationGallbladder Cancer Detection in Ultrasound Images based on YOLO and Faster R-CNN
Medical image analysis is a significant application of artificial intelligence for disease diagnosis. A crucial step in this process is the identification of regions of interest within the images. This task can be automa…
Cancer ClassificationGallbladder Cancer DetectionMedical Image Analysisobject-detection+1Leveraging Latents for Efficient Thermography Classification and Segmentation
Breast cancer is a prominent health concern worldwide, currently being the secondmost common and second-deadliest type of cancer in women. While current breast cancer diagnosis mainly relies on mammography imaging, in re…
Cancer ClassificationClassificationFeature EngineeringSegmentationPrimary liver cancer classification from routine tumour biopsy using weakly supervised deep learning
The diagnosis of primary liver cancers (PLCs) can be challenging, especially on biopsies and for combined hepatocellular-cholangiocarcinoma (cHCC-CCA). We automatically classified PLCs on routine-stained biopsies using a…
Cancer ClassificationClusteringDiagnosticWeakly-supervised LearningPredictive Modeling for Breast Cancer Classification in the Context of Bangladeshi Patients: A Supervised Machine Learning Approach with Explainable AI
Breast cancer has rapidly increased in prevalence in recent years, making it one of the leading causes of mortality worldwide. Among all cancers, it is by far the most common. Diagnosing this illness manually requires si…
Cancer ClassificationExhaustive Exploitation of Nature-inspired Computation for Cancer Screening in an Ensemble Manner
Accurate screening of cancer types is crucial for effective cancer detection and precise treatment selection. However, the association between gene expression profiles and tumors is often limited to a small number of bio…
Cancer ClassificationDiversityEnsemble Learningfeature selection+1A Novel Approach to Breast Cancer Histopathological Image Classification Using Cross-Colour Space Feature Fusion and Quantum-Classical Stack Ensemble Method
Breast cancer classification stands as a pivotal pillar in ensuring timely diagnosis and effective treatment. This study with histopathological images underscores the profound significance of harnessing the synergistic c…
Cancer ClassificationClassificationDiagnosticHistopathological Image Classification+3Benchmarking Image Transformers for Prostate Cancer Detection from Ultrasound Data
PURPOSE: Deep learning methods for classifying prostate cancer (PCa) in ultrasound images typically employ convolutional networks (CNNs) to detect cancer in small regions of interest (ROI) along a needle trace region. Ho…
BenchmarkingCancer ClassificationMultiple Instance LearningSpecificityA lightweight deep learning pipeline with DRDA-Net and MobileNet for breast cancer classification
Accurate and early detection of breast cancer is essential for successful treatment. This paper introduces a novel deep-learning approach for improved breast cancer classification in histopathological images, a crucial s…
Cancer ClassificationComputational EfficiencyBreast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainability
Cancer is one of the diseases that kill the most women in the world, with breast cancer being responsible for the highest number of cancer cases and consequently deaths. However, it can be prevented by early detection an…
Cancer ClassificationHyperparameter OptimizationTowards Out-of-Distribution Detection for breast cancer classification in Point-of-Care Ultrasound Imaging
Deep learning has shown to have great potential in medical applications. In critical domains as such, it is of high interest to have trustworthy algorithms which are able to tell when reliable assessments cannot be guara…
Cancer ClassificationOut-of-Distribution DetectionMV-Swin-T: Mammogram Classification with Multi-view Swin Transformer
Traditional deep learning approaches for breast cancer classification has predominantly concentrated on single-view analysis. In clinical practice, however, radiologists concurrently examine all views within a mammograph…
Cancer Classificationimage-classificationImage ClassificationExplainable Contrastive and Cost-Sensitive Learning for Cervical Cancer Classification
This paper proposes an efficient system for classifying cervical cancer cells using pre-trained convolutional neural networks (CNNs). We first fine-tune five pre-trained CNNs and minimize the overall cost of misclassific…
Cancer ClassificationContrastive LearningBeyond Traditional Approaches: Multi-Task Network for Breast Ultrasound Diagnosis
Breast Ultrasound plays a vital role in cancer diagnosis as a non-invasive approach with cost-effective. In recent years, with the development of deep learning, many CNN-based approaches have been widely researched in bo…
Anomaly ClassificationCancer ClassificationGPUSegmentation+1Skin Cancer Segmentation and Classification Using Vision Transformer for Automatic Analysis in Dermatoscopy-based Non-invasive Digital System
Skin cancer is a global health concern, necessitating early and accurate diagnosis for improved patient outcomes. This study introduces a groundbreaking approach to skin cancer classification, employing the Vision Transf…
Cancer ClassificationDeep LearningSkin Cancer ClassificationSkin Cancer SegmentationA Polarization and Radiomics Feature Fusion Network for the Classification of Hepatocellular Carcinoma and Intrahepatic Cholangiocarcinoma
Classifying hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC) is a critical step in treatment selection and prognosis evaluation for patients with liver diseases. Traditional histopathological diag…
Cancer ClassificationMedical Image AnalysisPrognosisMulti-stages attention Breast cancer classification based on nonlinear spiking neural P neurons with autapses
Breast cancer(BC) is a prevalent type of malignant tumor in women. Early diagnosis and treatment are vital for enhancing the patients' survival rate. Downsampling in deep networks may lead to loss of information, so for …
Cancer ClassificationA Unified Multi-Phase CT Synthesis and Classification Framework for Kidney Cancer Diagnosis with Incomplete Data
Multi-phase CT is widely adopted for the diagnosis of kidney cancer due to the complementary information among phases. However, the complete set of multi-phase CT is often not available in practical clinical applications…
Cancer ClassificationClassificationLesion Segmentation