Papers Cancer Classification
“Cancer Classification” 태그가 달린 논문 260편 · 필터 해제
Adversarial Augmentation for Enhancing Classification of Mammography Images
Supervised deep learning relies on the assumption that enough training data is available, which presents a problem for its application to several fields, like medical imaging. On the example of a binary image classificat…
Cancer ClassificationClassificationGeneral ClassificationImage Augmentation+2Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applications
As much as data science is playing a pivotal role everywhere, healthcare also finds it prominent application. Breast Cancer is the top rated type of cancer amongst women; which took away 627,000 lives alone. This high mo…
AttributeCancer ClassificationClassificationGeneral ClassificationSkin Lesion Synthesis with Generative Adversarial Networks
Skin cancer is by far the most common type of cancer. Early detection is the key to increase the chances for successful treatment significantly. Currently, Deep Neural Networks are the state-of-the-art results on automat…
Cancer ClassificationGeneral ClassificationMedical Image GenerationSkin Cancer ClassificationFeasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo imaging would be required. For example,…
Cancer ClassificationColon Cancer Detection In Confocal Laser Microscopy ImagesGeneral ClassificationTransfer LearningBreast Cancer Classification from Histopathological Images with Inception Recurrent Residual Convolutional Neural Network
The Deep Convolutional Neural Network (DCNN) is one of the most powerful and successful deep learning approaches. DCNNs have already provided superior performance in different modalities of medical imaging including brea…
Cancer ClassificationClassificationGeneral ClassificationObject RecognitionDermatologist Level Dermoscopy Skin Cancer Classification Using Different Deep Learning Convolutional Neural Networks Algorithms
In this paper, the effectiveness and capability of convolutional neural networks have been studied in the classification of 8 skin diseases. Different pre-trained state-of-the-art architectures (DenseNet 201, ResNet 152,…
Cancer ClassificationDiagnosticGeneral ClassificationSkin Cancer ClassificationConditional Infilling GANs for Data Augmentation in Mammogram Classification
Deep learning approaches to breast cancer detection in mammograms have recently shown promising results. However, such models are constrained by the limited size of publicly available mammography datasets, in large part …
Breast Cancer DetectionCancer ClassificationClassificationData Augmentation+1Domain Adaptation for Deviating Acquisition Protocols in CNN-based Lesion Classification on Diffusion-Weighted MR Images
End-to-end deep learning improves breast cancer classification on diffusion-weighted MR images (DWI) using a convolutional neural network (CNN) architecture. A limitation of CNN as opposed to previous model-based approac…
Cancer ClassificationDomain AdaptationGeneral ClassificationLesion ClassificationG2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis of nephropathy. As the difference between different subcategories is subtle, doctors often refer to slides from different staining methods to make …
Cancer ClassificationClassificationDecision MakingGeneral Classification+1DeepSDCS: Dissecting cancer proliferation heterogeneity in Ki67 digital whole slide images
Ki67 is an important biomarker for breast cancer. Classification of positive and negative Ki67 cells in histology slides is a common approach to determine cancer proliferation status. However, there is a lack of generali…
Cancer ClassificationCell SegmentationGeneral ClassificationSegmentation+1Context-Aware Learning using Transferable Features for Classification of Breast Cancer Histology Images
Convolutional neural networks (CNNs) have been recently used for a variety of histology image analysis. However, availability of a large dataset is a major prerequisite for training a CNN which limits its use by the comp…
Cancer ClassificationClassificationClassification Of Breast Cancer Histology ImagesGeneral ClassificationTree Ensembles with Rule Structured Horseshoe Regularization
We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu (2008) where rules from decision trees and lin…
Cancer ClassificationGeneral ClassificationregressionComparison among dimensionality reduction techniques based on Random Projection for cancer classification
Random Projection (RP) technique has been widely applied in many scenarios because it can reduce high-dimensional features into low-dimensional space within short time and meet the need of real-time analysis of massive d…
Cancer ClassificationClassificationDimensionality Reductionfeature selection+1Multi-Organ Cancer Classification and Survival Analysis
Accurate and robust cell nuclei classification is the cornerstone for a wider range of tasks in digital and Computational Pathology. However, most machine learning systems require extensive labeling from expert pathologi…
Cancer ClassificationClassificationGeneral ClassificationNuclei Classification+2Differential Evolution with Generalized Mutation Operator for Parameters Optimization in Gene Selection for Cancer Classification
Differential Evolution (DE) proved to be one of the most successful evolutionary algorithms for global optimization purposes in continuous problems. The core operator in DE is mutation which can provide the algorithm wit…
Cancer ClassificationEvolutionary Algorithmsfeature selectionGeneral Classification+1Gene selection for cancer classification using a hybrid of univariate and multivariate feature selection methods
Various approaches to gene selection for cancer classification based on microarray data can be found in the literature and they may be grouped into two categories: univariate methods and multivariate methods. Univariate …
Cancer Classificationfeature selectionGeneral ClassificationAn evaluation of DNA-damage response and cell-cycle pathways for breast cancer classification
Accurate subtyping or classification of breast cancer is important for ensuring proper treatment of patients and also for understanding the molecular mechanisms driving this disease. While there have been several gene si…
Cancer Classificationfeature selectionPrognosismiRNA and Gene Expression based Cancer Classification using Self- Learning and Co-Training Approaches
miRNA and gene expression profiles have been proved useful for classifying cancer samples. Efficient classifiers have been recently sought and developed. A number of attempts to classify cancer samples using miRNA/gene e…
Cancer ClassificationGeneral ClassificationSelf-LearningFeature vector regularization in machine learning
Problems in machine learning (ML) can involve noisy input data, and ML classification methods have reached limiting accuracies when based on standard ML data sets consisting of feature vectors and their classes. Greater …
BIG-bench Machine LearningCancer ClassificationDenoisingGeneral Classification+1Probabilistic Multi-Task Feature Selection
Recently, some variants of the $l_1$ norm, particularly matrix norms such as the $l_{1,2}$ and $l_{1,\infty}$ norms, have been widely used in multi-task learning, compressed sensing and other related areas to enforce spa…
Cancer Classificationcompressed sensingfeature selectionMulti-Task Learning