Neural network classifies liver cancer patients based on genomic data.
problem Classifying liver cancer patients into high-risk and low-risk groups.
method Data expansion using wavelet analysis, compression of wavelet coefficients, training a neural network model.
result The neural network model accurately classifies patients without survival time information.
Omics-GAN uses GANs to generate synthetic multi-omics data for improved disease prediction.
problem Limited sample sizes, noise, and heterogeneity in multi-omics data reduce predictive power.
method Omics-GAN is a GAN-based framework that generates high-quality synthetic multi-omics profiles.
result Synthetic datasets consistently improved prediction accuracy compared to original omics profiles.
Novel fusion network combines polarization and radiomics features for liver cancer classification.
problem Challenges in histopathological diagnosis of HCC and ICC.
method Two-tier fusion approach: feature-level and classification-level.
result Significantly enhances classification accuracy, even at reduced resolutions.
AI system helps clinicians assess liver metastases quickly and with explanations.
problem Manual assessment of metastases is time-consuming and subjective.
method Interactive AI system with model interpretability.
result AI-assisted assessment improves efficiency and provides explanations.
We present a novel method for extracting cancer signatures by applying statistical risk models (http://ssrn.com/abstract=2732453) from quantitative finance to cancer genome data. Using 1389 whole genome sequenced samples from 14 cancers, we identify an "overall" mode of somatic mutational noise. We give a prescription …
New *K-means method finds cancer signatures without NMF.
problem Identifying cancer signatures from genome data.
method Applying *K-means clustering to 1,389 cancer samples.
result 3 cancers lack cluster-like structures, 2 have high correlations.
Novel unsupervised method for fast 3D image registration using cycle-consistent CNN.
problem Medical image registration for cancer diagnosis.
method Unsupervised deep learning using cycle-consistent CNN for deformable registration.
result Very precise 3D image registration within a few seconds, improving cancer size estimation.
Paper proposes clustering model for ICC based on histologic patterns.
problem Challenges in grading rare cancers like ICC due to small sample sizes and difficulty in extracting patterns.
method Unsupervised deep convolutional autoencoder clustering model trained on 246 ICC digitized slides.
result Three clusters significantly associated with recurrence-free survival in Cox-proportional hazard models.
Developed mlf-core for deterministic machine learning.
problem Ensuring machine learning models are deterministic for verification.
method Formulated requirements, developed mlf-core ecosystem, tested various models.
result Demonstrated deterministic models in biomedical fields.
GANs generate synthetic medical images to enhance CNN liver lesion classification.
problem Lack of large annotated medical datasets for training CNNs.
method Used Generative Adversarial Networks (GANs) to create synthetic medical images for augmentation.
result Synthetic data augmentation improved CNN performance in liver lesion classification.
Self-supervised method improves CBIR of CT liver images.
problem Limited labeled data and lack of transparency in deep CBIR systems.
method Proposes a self-supervised learning framework with domain-knowledge integration.
result Improved performance and generalization across datasets.
Paper proposes abstention in machine learning for better liver disease diagnosis accuracy.
problem Improving accuracy in automated liver disease diagnosis using ultrasound images.
method Implemented a machine learning model with abstention capability, using stochastic gradient solver.
result The proposed abstention model outperforms existing models in liver disease classification.
Background: Many authors have described MELD as a predictor of short-term mortality in the liver transplantation waiting list. However MELD score accuracy to predict long term mortality has not been statistically evaluated. Objective: The aim of this study is to analyze the MELD score as well as other variables as a pr…
Data mining techniques on the biological analysis are spreading for most of the areas including the health care and medical information. We have applied the data mining techniques, such as KNN, SVM, MLP or decision trees over a unique dataset, which is collected from 16,380 analysis results for a year. Furthermore we h…
Deep learning methods improve Bayesian inversion for efficient 3D imaging.
problem Efficiently solving large-scale inverse problems in medical imaging.
method Two novel deep learning approaches: WGAN with mini-discriminator and neural network loss function.
result Both methods compute posterior mean and standard deviation efficiently, demonstrating promising performance in 3D imaging.
Liver transplant patients have a 25% chance of developing diabetes within 5 years.
problem Predicting new onset diabetes after liver transplant to improve patient care.
method Comparison of time-to-event prediction models and classifiers, including regularized Cox proportional-hazards model.
result Regularized Cox proportional-hazards model achieved high Concordance Index of 0.863.
Estimates complex dependency structures in multi-omics data.
problem Graphical model estimation from multi-omics data with scalability and consistency.
method Pseudolikelihood-based graphical model framework with ℓ1-penalized empirical risk. result Estimates partial correlation network from dual-omic liver cancer data.
Set classification problems arise when classification tasks are based on sets of observations as opposed to individual observations. In set classification, a classification rule is trained with N sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…
PNNs improve treatment outcomes in TAVR and liver trauma.
problem Improving treatment outcomes in medical procedures.
method Multimodal Prescriptive Neural Networks (PNNs) combining optimization and machine learning.
result PNNs significantly improve estimated outcomes in medical procedures.
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Ed…
Paper uses ML to classify liver diseases from clinical data.
problem Classifying liver diseases from clinical data.
method Multiple imputation, PCA, data visualizations, binary classifier algorithms (ANN, RF, SVM).
result SVM showed better accuracy (98.23%).
Augmented CT slices improve liver lesion classification accuracy.
problem Improving pixel-wise classification of hepatic lesions and normal liver tissues.
method Anatomical data augmentation using adjacent CT slices for training a U-net network.
result Improvement of 3% in success rate, 5% in classification accuracy, and 4% in Dice.
Estimates cost savings from early cancer diagnosis.
problem Improving early cancer diagnosis to reduce treatment costs.
method Combining published cancer treatment cost estimates by stage with incidence rates by stage at diagnosis, and extrapolating to other cancer sites.
result Estimates U.S. national annual treatment cost-savings from early cancer diagnosis in the trillions.
Method cleans noisy training labels for biomedical data.
problem Accurately labeling biomedical data is challenging.
method Reliability-based training data cleaning with inductive conformal prediction.
result Significant enhancements in classification performance across multiple tasks.
Model predicts anti-cancer drug responses using gene and molecular data.
problem Expensive and time-consuming cancer drug discovery and tailoring.
method Uses variational autoencoders and multi-layer perceptrons to encode gene expression and drug data.
result High average R2 of 0.83 and 0.845 in predicting drug responses for breast and pan-cancer cell lines, respectively. Study uses machine learning to predict heart failure in cancer patients.
problem Early detection of cancer patients at risk for cardiotoxicity.
method Examined four machine learning algorithms on 143,199 cancer patients.
result Gradient boosting model achieved best AUC score of 0.9077.
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.
Estimates deformation of elastic objects using neural networks from few observations.
problem Estimating deformation of elastic objects using limited data.
method Learning approach with a neural network to estimate entire deformation from few observations.
result Average estimation error of 0.041 mm for human liver model under significant deformation.
Deep learning models improve cancer detection and typing classification from gene expression data.
problem Challenges in establishing specificity for cancer diagnosis using gene expression data.
method Developed deep learning models using mRNA datasets for cancer detection and typing classification.
result Achieved 98% accuracy in cancer detection and 18 out of 32 cancer-typing classifications over 90% accuracy.
Study uses Apple ML to accurately detect and classify lung cancer.
problem Accurate diagnosis and sub-classification of non-small cell lung cancer.
method Evaluation of Apple Create ML module on histopathological images.
result 100% detection and successful subclassification of non-small cell lung cancer.
Study predicts 10-year survival rates for breast cancer patients.
problem Predicting long-term survival of breast cancer patients.
method Machine learning approaches to assess survival rates.
result Improved accuracy in predicting 10-year survival.
Cancer patients admitted to ICU had improved survival over 10 years.
problem To assess changes in survival of cancer patients admitted to ICU over 10 years.
method Retrospective analysis of MIMIC-III database, adjusted for confounders using logistic regression.
result Cancer patients had significantly lower 28-day and 1-year mortality rates over 10 years.
Paper identifies key CpG methylation sites for breast cancer.
problem Early detection and treatment of breast cancer.
method Used machine learning on TCGA dataset to classify cancer vs. non-cancer samples.
result Reduced model with 25 key CpG sites achieves over 94% accuracy.
Noise-filtering improves cancer drug sensitivity prediction.
problem Improving accuracy of cancer drug sensitivity predictions.
method Integrates numerical linear algebra and information retrieval techniques to filter out noisy cancer cell lines.
result Our approach yields the highest AUC on clinical trial data.
Data mining techniques predict breast cancer types with high accuracy.
problem Early detection of breast cancer to reduce mortality rates.
method Twelve classification algorithms applied to the Breast Cancer Wisconsin dataset.
result High accuracy in predicting malignant and benign breast cancer.
Study examines perceptions and attitudes about breast cancer on Twitter.
problem Understanding public perceptions and attitudes towards breast cancer on social media.
method Identified and collected tweets, used topic modeling and sentiment analysis.
result Identified themes and quantified users' perceptions and emotions about breast cancer.
Machine learning clusters mutations in cancer exomes, improving diagnostic speed and cost.
problem Extracting stable mutation structures from cancer exome data for early diagnostics.
method Statistically deterministic machine learning algorithm *K-means applied to exome samples.
result Majority of cancer types exhibit stable mutation clustering, while NMF methods are unstable.
Deep learning predicts breast cancer with high accuracy from patient data.
problem Early detection of breast cancer from patient data.
method Feature selection and k-fold Monte Carlo cross-validation using deep learning.
result Deep learning model effectively distinguishes between cancer and healthy patients.
Machine learning accurately diagnoses cancer from whole genome sequencing data.
problem Accurate cancer diagnosis at all stages.
method Novel MLAC (Machine Learning Against Cancer) method using next-gen RNA sequencing.
result Perfect precision, sensitivity, and specificity achieved for most tumor types.
Neural networks improve cancer risk prediction from family history data.
problem Improving cancer risk prediction from family history data using machine learning.
method Developed and trained neural network models on large pedigrees to predict hereditary cancers.
result Neural networks can achieve nearly optimal prediction performance and outperform traditional models in misreported data.
Framework predicts mortality risk in MAFLD subjects.
problem Lack of mortality prediction methods for MAFLD subjects.
method Artificial Intelligence-based framework MAFUS using ML algorithms.
result Support Vector Machines (SVM) is the best model for mortality prediction.
Machine learning detects metastatic breast cancer cases from linked EMR and cancer registry data.
problem Lack of metastatic recurrence data in cancer registries and EMRs.
method Semi-supervised machine learning on linked EMR and CCR data.
result Model achieved high accuracy in detecting metastatic breast cancer cases.
L-Perceptron improves breast cancer diagnosis and survival prediction.
problem Improving early prognosis and survival prediction rates for breast cancer.
method Proposes a novel type of perceptron (L-Perceptron) for better accuracy and sensitivity.
result Achieves 97.42% and 98.73% accuracy and sensitivity in Wisconsin Breast Cancer dataset.
System automates identification of cancer drug repurposing from PubMed.
problem Manual extraction of cancer drug repurposing evidence from scientific publications is infeasible.
method NLP pipeline including querying, filtering, entity extraction, classification, and study type classification.
result Automated system extracts cancer drug repurposing evidence from PubMed abstracts.
PathologyGAN learns deep representations of cancer tissue images.
problem Limited high-quality labels for cancer tissue images.
method Developed a GAN framework for unsupervised learning of cancer tissue phenotypes.
result Generated high-quality images with interpretable latent space.
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.
Deep autoencoder predicts cancer types from DNA methylation patterns.
problem Differentiating cancer types based on DNA methylation states.
method Deep learning system with CpG island state classification and statistical methods.
result Overall Sensitivity of 88.24%, Specificity of 83.33%, Accuracy of 84.75%.
Deep CNN model improves breast cancer screening exam classification.
problem Improving accuracy in breast cancer screening exam classification.
method Localization-based deep CNN trained on 200,000 exams.
result AUC of 0.919 in predicting malignancy, reducing error rate by 23%.