Paper presents an EMD-based feature extraction method for microwave breast cancer detection.
problem Improving breast cancer detection using machine learning with robust feature extraction.
method Empirical Mode Decomposition (EMD) for feature extraction, compared to PCA.
result Combined EMD and PCA features improve detection performance with an ensemble selection-based classifier.
Improved detection of brain tumours in MRIs using latent space dissimilarities.
problem Detecting tumours in brain MRIs using unsupervised learning.
method Slice-wise semi-supervised method based on dissimilarity between latent representations of images and their reconstructions.
result Improved detection results with higher resolution images and better reconstructions.
Complete CAD system for breast cancer detection from mammograms.
problem Reducing human errors in breast cancer detection.
method Algorithm development for image enhancement, mass and microcalcifications detection, and system architecture design.
result Novel algorithms for mass and microcalcifications detection with superior accuracy.
Deep object detection improves mitotic nucleus detection in breast cancer biopsies.
problem Challenges in automated mitotic nucleus detection in breast cancer histopathological images.
method Adapted Mask R-CNN for deep object detection, initially selects candidate regions with maximum recall, refines them with multi-object loss function.
result Improved discrimination ability (F-score of 0.86) and significant precision (0.86) for mitotic nuclei compared to two-stage models.
Transfer learning from breast histopathology improves prostate cancer detection.
problem Insufficient prostate histopathology datasets for training.
method Transfer learning from breast histopathology images.
result Proposed approach outperforms transfer learning from ImageNet dataset.
Paper presents a breast cancer detection model using ELM-RBF.
problem Detecting breast cancer using mammography with high cost and side effects.
method Multilayer fuzzy expert system with ELM-RBF model.
result ELM-RBF model outperforms linear-SVM model in accuracy, precision, sensitivity, specificity, and other metrics.
Paper uses deep learning to detect subtle breast cancer signs.
problem Detecting subtle architectural distortion in mammograms.
method Data augmentation for training a Convolutional Neural Network (CNN).
result CNN trained on augmented data detected AD with AUC = 0.74.
An algorithm reduces breast cancer detection data complexity using effect sizes.
problem Improving accuracy in breast cancer detection.
method Statistical feature selection and SVM classifier with linear kernel.
result SVM classifier achieved over 90% accuracy.
DEER network improves few-view breast CT image reconstruction efficiency and quality.
problem Efficient and high-quality few-view breast CT image reconstruction.
method Deep Efficient End-to-end Reconstruction (DEER) network with low model complexity.
result DEER network achieves competitive image quality with significantly fewer parameters compared to state-of-the-art methods.
DCGANs generate realistic breast masses for mammography.
problem Lack of labelled data and imbalanced datasets in medical imaging.
method DCGANs trained on mammographic images to generate synthetic masses.
result DCGANs improve lesion detection in mammography by ~0.09 F1 score.
ML algorithms improve breast cancer detection accuracy.
problem Improving accuracy in breast cancer detection using machine learning.
method Comparison of six ML algorithms (GRU-SVM, Linear Regression, MLP, NN, Softmax Regression, SVM) on the WDBC dataset.
result ML algorithms achieve high accuracy (>90%) in classifying breast cancer.
In this paper, we describe a dataset relating to cellular and physical conditions of patients who are operated upon to remove colorectal tumours. This data provides a unique insight into immunological status at the point of tumour removal, tumour classification and post-operative survival. Attempts are made to learn re…
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.
Paper optimizes sparse feature selection for cancer detection using GSVP and SVM.
problem Sparse feature selection for cancer detection.
method Regularized GSVP with proximal gradient descent, feature selection via SVM.
result Near-perfect balanced accuracy with few selected features.
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.
CAA finds correlations within a single set of variables, useful for anomaly detection and unsupervised learning.
problem Finding hidden parsimonious structures in data with multiple-to-multiple correlations.
method CAA extends sparse CCA to find multivariate correlations within a single set of variables.
result CAA can be used for anomaly detection and unsupervised learning of correlation structures.
Deep neural network improves cancer detection accuracy in breast screening.
problem Improving cancer detection accuracy in breast screening.
method Two-stage training procedure using patch-level and macroscopic labels.
result Neural network achieves AUC of 0.895 in predicting cancer presence.
Deep learning improves breast cancer detection in DOT.
problem Complex physics and ill-posedness in DOT reconstruction.
method Deep learning approach that learns non-linear photon scattering physics.
result Deep neural network accurately recovers optical anomalies.
Deep learning improves breast cancer detection on mammograms.
problem Improving accuracy of breast cancer detection in mammograms.
method End-to-end deep learning approach using convolutional networks.
result Deep learning method achieved high accuracy on various mammography datasets.
Proposes RCVs for explaining deep neural network predictions in medical images.
problem Need for explainable predictions in medical applications.
method Uses continuous concept measures as RCVs in neural network activation space.
result Nuclei texture is a relevant concept in breast cancer grading.
Tensor clustering reveals crosstalk mechanisms in breast cancer.
problem Understanding diverse breast cancer cell responses to ligands.
method Tensor-based clustering with algebraic constraints.
result Interpretable clusters of crosstalk mechanisms identified.
Custom NLP system extracts clinical data for breast cancer analysis.
problem Manual extraction of information from text-based medical records is tedious and requires specialized knowledge.
method Combines standard text mining techniques with advanced synonym detection for global analysis.
result Achieved good extraction accuracy for various concepts of interest without requiring existing corpora or ontologies.
Test assesses if a linear classifier is random or significant.
problem Determining if a linear classifier captures meaningful differences between classes.
method Proposes a homogeneity test related to linear separability, establishes upper bounds for p-values.
result Upper bounds for p-values are highly accurate for normally distributed samples.
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.
KLIC combines multiple datasets for clustering, down-weighting noisy data.
problem Robustness of COCA in noisy or conflicting datasets.
method Multiple Kernel Learning for Integrative Clustering.
result KLIC down-weights noisy datasets, improving clustering accuracy.
Study classifies mammographic breast density using residual learning.
problem Classifying mammographic breast density for breast cancer risk.
method Radiomics approach based on residual learning.
result Outstanding classification results with high accuracy.
Deep feature fusion improves mitosis counting accuracy.
problem Manual mitosis counting by pathologists is time-consuming and inconsistent.
method Combines Faster R-CNN for object detection with UNet segmentation features and RGB image features.
result Achieved an F-score of 0.508 on mitosis counting challenge dataset, outperforming state-of-the-art methods.
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.
Proposes a plastic neural memory model for better anomaly detection.
problem Static attention mechanisms limit NMNs in anomaly detection.
method Introduces dynamic connection weights for improved knowledge retrieval.
result Outperforms state-of-the-art in three medical anomaly detection tasks.
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.
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%.
Deep neural network classifies breast density from large dataset.
problem Classifying breast density for cancer screening.
method Used a large dataset of 200,000 breast cancer screening exams to train a convolutional neural network.
result Convolutional neural network performs comparably to human experts in classifying breast density.
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.
Deep neural network for breast cancer screening using multi-view images.
problem Improving accuracy in breast cancer detection using medical images.
method Developed a multi-view deep convolutional neural network for high-resolution medical images.
result The model achieves comparable performance to radiologists using original resolution images.
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.
We present a nonparametric Bayesian method for disease subtype discovery in multi-dimensional cancer data. Our method can simultaneously analyse a wide range of data types, allowing for both agreement and disagreement between their underlying clustering structure. It includes feature selection and infers the most likel…
Paper compares SVM and Bayesian Logistic Regression for breast cancer diagnosis.
problem Improving breast cancer diagnosis accuracy using machine learning.
method Used SVM, Bayesian Logistic Regression, and K-Nearest-Neighbors for classification.
result SVM outperformed other classifiers, closely matched by Bayesian Logistic Regression.
Deep learning system classifies breast cancer biopsy images with high accuracy.
problem Accurately differentiate between normal and cancerous breast tissue.
method Convolutional capsule network for four types of breast biopsy images.
result Cross-validation accuracy of 0.87 with high sensitivity.
Bayesian variational inference improves medical image segmentation confidence.
problem Improving interpretability and confidence in deep learning models for medical image segmentation.
method Encoder-decoder architecture based on variational inference for segmenting brain tumor images.
result The model segments brain tumors with both aleatoric and epistemic uncertainty.
Deep learning network matches radiologists in breast cancer segmentation.
problem Automating radiologist-level cancer segmentation from breast MRI.
method 3D U-Net architecture trained on 382,290 breast scans, compared to 255,500 benign cases.
result Network performance matched radiologists' on 2D segmentation of breast cancers.
Deep learning model explains breast cancer subtypes using logistic regression.
problem Clarifying the mechanisms of breast cancer subtypes for better treatment.
method Developed a PWL model that generates custom-made logistic regression for each patient.
result The PWL model reveals genes relevant to cell cycle-related pathways.
The paper compares Bayesian trees, Cox models, and random forests for breast cancer survival data.
problem Modeling survival data with nonlinear and additive effects.
method Bayesian Additive Regression Trees, Cox proportional hazards, and Random Survival Forests.
result Bayesian trees outperform other models in terms of bias and prediction accuracy.
Unified framework improves gene prioritization in disease studies.
problem Identifying genes involved in diseases using heterogeneous biological data.
method Network propagation-based gene prioritization with integrated biological information.
result Significant improvements in prioritizing genes not identified by traditional methods.
A benchmark evaluates ioUS-to-MR synthesis methods for brain tumor surgery.
problem Difficult interpretation of ioUS images for brain tumor surgery.
method Six generators trained under four inference regimes and two targets on public data.
result SynDiff-2.5D best preserved downstream segmentation (U_Dice=0.55).
Novel framework controls FDR in high-dimensional, dependent data.
problem FDR control failure in high-dimensional, dependent data.
method Dependency-aware T-Rex selector integrating hierarchical graphical models and martingale theory.
result First to control FDR in high-dimensional, dependent data.
DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.
problem Imbalanced ultrasound modalities in diagnosing breast cancer.
method Integrates LUPI and MMD into a deep transfer learning framework.
result Outperforms state-of-the-art algorithms in BUS-based CAD.
Mammography is the most effective and available tool for breast cancer screening. However, the low positive predictive value of breast biopsy resulting from mammogram interpretation leads to approximately 70% unnecessary biopsies with benign outcomes. Data mining algorithms could be used to help physicians in their dec…
New models compare mammograms to improve cancer diagnosis.
problem Improving cancer diagnosis accuracy by comparing recent and prior mammograms.
method Proposed neural network models trained on over 665,000 pairs of images.
result Best model achieves AUC of 0.866 in predicting malignancy.