System accurately detects lung cancer from CT images.
problem Early and accurate detection of lung cancer.
method Developed algorithms using a dataset of CT images.
result Accuracy of 72.2% on test dataset.
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48 results for “Lung Cancer Classification”
MEM learns set functions from permutation-invariant data.
problem Learning from sets of instances with labels only on sets, not instances.
method Memory-based Exchangeable Model (MEM) with self-attention mechanism.
result Achieved 84.84% accuracy on lung cancer classification.
Using Apple Machine Learning Algorithms to Detect and Subclassify Non-Small Cell Lung Cancerq-bio.QM
Lung cancer continues to be a major healthcare challenge with high morbidity and mortality rates among both men and women worldwide. The majority of lung cancer cases are of non-small cell lung cancer type. With the advent of targeted cancer therapy, it is imperative not only to properly diagnose but also sub-classify …
Study identifies biomarkers for lung cancer in female non-smokers.
problem Identifying prognostic biomarkers for stage III NSCLC in non-smoking females.
method Gene expression profiling and XGBoost machine learning algorithm.
result Top biomarkers validated in literature, with AUC score of 0.835.
Lung cancer is the leading cause of cancer-related death worldwide. Early diagnosis of pulmonary nodules in Computed Tomography (CT) chest scans provides an opportunity for designing effective treatment and making financial and care plans. In this paper, we consider the problem of diagnostic classification between beni…
Survival month for non-small lung cancer patients depend upon which stage of lung cancer is present. Our aim is to identify smoking specific gene expression biomarkers in the prognosis of lung cancer patients. In this paper, we introduce the network elastic net, a generalization of network lasso that allows for simulta…
Deep Learning of Semi-Competing Risk Data via a New Neural Expectation-Maximization Algorithmstat.ML
New algorithm predicts lung cancer progression and mortality.
problem Predicting semi-competing risk outcomes in lung cancer.
method Neural Expectation-Maximization algorithm for multi-state outcomes.
result Estimates non-parametric baseline hazards and risk functions.
Proposes ConRad model for lung cancer classification using radiomics and interpretable machine learning.
problem Lack of interpretability in deep neural networks for cancer diagnosis.
method Integration of radiomics and DNN-predicted biomarkers in interpretable classifiers (ConRad).
result ConRad models outperform CNNs in five-fold cross-validation.
Penalized Deep Partially Linear Cox Models with Application to CT Scans of Lung Cancer Patientsstat.ML
Proposes a new model to analyze CT scans for lung cancer patients.
problem Analyzing survival risks of lung cancer patients using CT scans.
method Penalized Deep Partially Linear Cox Model (Penalized DPLC) incorporating SCAD penalty and deep neural network.
result The model effectively selects important texture features and estimates nonparametric components.
New framework distinguishes lung cancer subtypes using MALDI mass spectrometry.
problem Distinguishing between adenocarcinoma and squamous cell carcinoma subtypes in lung cancer.
method Supervised topological data analysis on MALDI mass spectrometry imaging data.
result The proposed framework successfully classifies lung cancer subtypes with competitive results.
Computed tomography (CT) examinations are commonly used to predict lung nodule malignancy in patients, which are shown to improve noninvasive early diagnosis of lung cancer. It remains challenging for computational approaches to achieve performance comparable to experienced radiologists. Here we present NoduleX, a syst…
Paper proposes inference method for high-dimensional censored quantile regression.
problem Identifying heterogeneous effects of high-dimensional genetic biomarkers on survival outcomes.
method Combines low-dimensional model estimates based on multi-sample splittings and variable selection.
result Proposed estimator is consistent and asymptotically follows a Gaussian process.
We estimate treatment cost-savings from early cancer diagnosis. For breast, lung, prostate and colorectal cancers and melanoma, which account for more than 50% of new incidences projected in 2017, we combine published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis. We extrapolate to…
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train. A representative example is automated lung cancer diagnosis, where nodule images need to be classified as suspicious or benign. In this work we propose an automatic synthetic lung …
New method uses probabilistic independence to discover disease signatures from medical records.
problem Insufficiently precise diagnosis of clinical disease leading to treatment failures.
method Unsupervised machine learning using probabilistic independence to disentangle disease patterns.
result Inferred 2000 clinical disease signatures from medical records, improving cancer prediction.
A Deep Autoencoder System for Differentiation of Cancer Types Based on DNA Methylation Stateq-bio.QM
A Deep Autoencoder based content retrieval algorithm is proposed for prediction and differentiation of cancer types based on the presence of epigenetic patterns of DNA methylation identified in genetic regions known as CpG islands. The developed deep learning system uses a CpG island state classification sub-system to …
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentationeess.IV
U-Det improves lung nodule segmentation in CT images.
problem Challenging shapes and surroundings of lung nodules in CT images.
method End-to-end deep learning with Bi-FPN, Mish activation, and class weights.
result U-Det achieves 82.82% Dice similarity coefficient, comparable to human experts.
We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We apply *K-means to extracting cancer signatures from genome data w…
Among American women, the rate of breast cancer is only second to lung cancer. An estimated 12.4% women will develop breast cancer over the course of their lifetime. The widespread use of social media across the socio-economic spectrum offers unparalleled ways to facilitate information sharing, in particular as it pert…
Deep learning has shown remarkable results for image analysis and is expected to aid individual treatment decisions in health care. To achieve this, deep learning methods need to be promoted from the level of mere associations to being able to answer causal questions. We present a scenario with real-world medical image…
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans. However, the size of a nodule is very diverse and can range anywhere between 3 and 30 millimeters. The high variation of nodule sizes makes classifying them a difficult and cha…
We propose a novel "tree-averaging" model that utilizes the ensemble of classification and regression trees (CART). Each constituent tree is estimated with a subset of similar data. We treat this grouping of subsets as Bayesian ensemble trees (BET) and model them as an infinite mixture Dirichlet process. We show that B…
Paper proposes a method to estimate confidence bands for survival random forests.
problem No statistically valid and computationally feasible approach for estimating confidence bands for survival random forests.
method Extending recent developments in infinite-order incomplete U-statistics, the paper proposes an unbiased confidence band estimation.
result The proposed method accurately estimates the confidence band and achieves desired coverage rate.
New method groups genetic data into coherent topics for disease insights.
problem Analyzing large, multi-dimensional genetic data sets.
method Conditional Hierarchical Bayesian Tucker Decomposition for genetic data analysis.
result Our models are more coherent than baseline models.
A Pathology report is arguably one of the most important documents in medicine containing interpretive information about the visual findings from the patient's biopsy sample. Each pathology report has a retention period of up to 20 years after the treatment of a patient. Cancer registries process and encode high volume…
Bayesian SPCA method tackles orthogonality constraint with spike and slab prior.
problem Bayesian SPCA method for high-dimensional data with orthogonality constraint.
method Parameter-expanded coordinate ascent variational inference (PX-CAVI) with spike and slab prior.
result PX-CAVI algorithm outperforms existing SPCA approaches in performance.
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning. While the variation in the appearance of the nodules remains large, there is a need for a fast and robust computer aided system. In this work, we propose an end-to-end tra…
The study of high-throughput genomic profiles from a pharmacogenomics viewpoint has provided unprecedented insights into the oncogenic features modulating drug response. A recent screening of ~1,000 cancer cell lines to a collection of anti-cancer drugs illuminated the link between genotypes and vulnerability. However,…
3D Axial-Attention improves lung nodule classification accuracy.
problem Limited 3D attention in existing methods.
method Proposes 3D Axial-Attention network with 3D positional encoding.
result 3D Axial-Attention achieves state-of-the-art performance.
A novel approach for 3D lung nodule segmentation using adaptive ROI and multi-view residual learning.
problem Inaccurate nodule segmentation due to fixed ROI and redundant structures.
method Two-stage approach: 2D ROI patch-wise investigation with adaptive ROI strategy, followed by 2D and 3D VOI investigation with deep residual U-Net.
result Significantly robust and accurate nodule segmentation compared to previous methods.
The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approxima…
New method estimates treatment effects over time for survival data, improving accuracy and smoothness.
problem Estimating treatment effects over time for survival data with left truncation and right censoring.
method surv-iTMLE, a targeted learning procedure for estimating conditional survival probabilities.
result surv-iTMLE outperforms existing methods in bias and smoothness of time-varying effect estimates.
The aim of this contribution is to derive a general matrix formula for the net period premium paid in more than one state. For this purpose we propose to combine actuarial technics with the graph optimization methodology. The obtained result is useful for example to more advanced models of dread disease insurances allo…
MCPCA analyzes shared factors across multiple data contexts.
problem No tools to recover shared factors across multiple contexts.
method Developed a theoretical and algorithmic framework (MCPCA).
result Reveals shared axes of variation across subsets of contexts.
A fundamental question in data analysis, machine learning and signal processing is how to compare between data points. The choice of the distance metric is specifically challenging for high-dimensional data sets, where the problem of meaningfulness is more prominent (e.g. the Euclidean distance between images). In this…
Deep neural network for cancer classification using autoencoders.
problem Cancer classification using molecular information.
method Using a Denoising Autoencoder (DAE) as weight initialization for a deep neural network, comparing two approaches: fixed weights and fine-tuning. Embedding strategies included encoding layers and complete autoencoder.
result Best F1 score of 98.04% for identifying thyroid cancer samples.
Early diagnosis of interstitial lung diseases is crucial for their treatment, but even experienced physicians find it difficult, as their clinical manifestations are similar. In order to assist with the diagnosis, computer-aided diagnosis (CAD) systems have been developed. These commonly rely on a fixed scale classifie…
Identification of Cancer -- Mesothelioma Disease Using Logistic Regression and Association Rulecs.CY
Malignant Pleural Mesothelioma (MPM) or malignant mesothelioma (MM) is an atypical, aggressive tumor that matures into cancer in the pleura, a stratum of tissue bordering the lungs. Diagnosis of MPM is difficult and it accounts for about seventy-five percent of all mesothelioma diagnosed yearly in the United States of …
Prediction of Malignant & Benign Breast Cancer: A Data Mining Approach in Healthcare Applicationscs.LG
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 mortality rate due to breast cancer does need attention, for early detection so that prevention can be…
Ensembling classification models based on phalanxes of variables with applications in drug discoverystat.ML
Statistical detection of a rare class of objects in a two-class classification problem can pose several challenges. Because the class of interest is rare in the training data, there is relatively little information in the known class response labels for model building. At the same time the available explanatory variabl…
With the increased affordability and availability of whole-genome sequencing, large-scale and high-throughput gene expression is widely used to characterize diseases, including cancers. However, establishing specificity in cancer diagnosis using gene expression data continues to pose challenges due to the high dimensio…
Development of a Machine-Learning System to Classify Lung CT Scan Images into Normal/COVID-19 Classeess.IV
System classifies lung CT scans into normal or COVID-19 using machine learning.
problem Detecting COVID-19 infection in lung CT scans.
method MLS with CBA+KE thresholding, feature extraction, selection, and classification.
result SVM with FFV achieved 89.80% detection accuracy.
This study improves lung tumor segmentation in mice MRI scans with nnU-Net, reducing annotation needs.
problem Accurate lung tumor segmentation in mice MRI scans for drug discovery.
method Optimized nnU-Net 3D model for lung tumor segmentation with minimal annotations.
result nnU-Net 3D models outperform 2D models in MRI mice scans, requiring fewer annotations.
Late diagnosis and high costs are key factors that negatively impact the care of cancer patients worldwide. Although the availability of biological markers for the diagnosis of cancer type is increasing, costs and reliability of tests currently present a barrier to the adoption of their routine use. There is a pressing…
A DenseNet model classifies metastatic cancer in medical images.
problem Classifying metastatic cancer in medical images efficiently and accurately.
method Proposes a DenseNet-based model for metastatic cancer classification on medical images.
result The proposed model outperformed other classical methods like Resnet34, Vgg19.
Lung segmentation from abnormal CXRs using data imputation.
problem Segmenting lungs from CXRs with high opacity caused by respiratory ailments.
method Modified CNN-based segmentation network with deep generative model for data imputation.
result The model can segment lungs from abnormal CXRs, extending to cases with extreme abnormalities.
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learningcs.CV
A-MIL improves histopathology image classification and localization.
problem Improving diagnosis of breast cancer through better interpretation of histopathology images.
method Frame image classification as multiple instance learning, use attention-based learning for localization.
result A-MIL achieves better localization without compromising classification accuracy.
The objectives of this "perspective" paper are to review some recent advances in sparse feature selection for regression and classification, as well as compressed sensing, and to discuss how these might be used to develop tools to advance personalized cancer therapy. As an illustration of the possibilities, a new algor…