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.
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 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.
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.
Machine learning models improve cancer type classification accuracy.
problem Early and accurate cancer diagnosis is challenging due to high costs and biological marker limitations.
method Assessed five machine learning algorithms for 17 cancer types using RNA-seq data.
result Ensemble algorithms achieve 100% accuracy in 14 out of 17 cancer types.
The study improves colorectal cancer survivability prediction by considering ethnicity.
problem Improving colorectal cancer survivability prediction using machine learning.
method Machine learning techniques applied to SEER cancer incidence database, comparing different ethnicities.
result Models perform better on single-ethnicity populations and provide different feature importance rankings.
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.
The paper classifies cervical cancer using various techniques and feature selection.
problem Classifying cervical cancer from a dataset with missing values and imbalance.
method Feature selection, over-sampling, under-sampling, dimensionality reduction, and classification techniques.
result Age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy.
This review chronicles AI algorithms for cervical cancer screening.
problem Automated screening of cervical cancer using AI methods.
method Analysis of various machine learning algorithms and clustering techniques.
result Holistic review of computational methods over time.
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…
Paper tackles cancer mutation data challenges by creating useful low-dimensional representations.
problem Challenges in analyzing and using cancer mutation data for classification and clustering.
method Flatsomatic: variational autoencoders (VAEs) to create latent representations of somatic profiles.
result VAE embeddings perform better than PCA for clustering and equally well for classification.
Proposes a multi-resolution model for prostate cancer classification using mpMRI.
problem Improving voxel-wise classification of prostate cancer using multi-parametric MRI data.
method Multi-resolution Super Learner framework combining local base learners at multiple resolutions and spatial Gaussian kernel smoothing.
result Enhanced voxel-wise classification of prostate cancer status and clinical significance.
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.
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.
Study improves cancer classification using gene selection and projection methods.
problem Overfitting in high-dimensional microarray datasets for cancer classification.
method FSWOR technique, random projection, Kendall test, ensemble classifiers, LDA projection, Naïve Bayes.
result Achieved a test score of 96%, significantly outperforming existing methods.
Deep learning improves tumor type classification accuracy.
problem Classifying cancer types based on DNA mutations is challenging.
method Deep transfer learning and fine-tuning of gene expression data.
result Significantly improved tumor type classification accuracy (78.3%) using DNA point mutations.
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 pathologists for each individual problem at hand, with no or limited abilities for knowledge transfer between…
AI detects oral pre-cancerous lesions with high accuracy.
problem Manual screening of oral cavity cancer is expensive and lacks specialists.
method Deep convolutional neural networks (DCNNs) using transfer learning.
result DCNN models achieve high accuracy in distinguishing between benign and pre-cancerous tongue lesions.
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.
New deep learning method classifies cancer cells from small training sets.
problem Small training sets in medical imaging.
method Hybrid of transfer learning and GANs.
result 90-99% accuracy in classifying healthy and cancer cells.
Robust cancer screening model using pre-trained ensembles for biomarkers.
problem Detecting early-stage cancer, especially in hard-to-diagnose cases like pancreatic cancer.
method Meta-trained Hyperfast model for robust classification, combined with ensembling of XGBoost and LightGBM.
result Achieved highest AUC of 0.9929 and robust performance on imbalanced datasets.
Monte-Carlo sampling improves histological image classification accuracy.
problem Histological image classification accuracy.
method Sequential Monte-Carlo method for patch sampling.
result Higher generalization performance compared to grid and uniform sampling.
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.
Deep learning improves cancer report classification accuracy.
problem Automatically assigning ICD-O3 codes to cancer reports.
method State-of-the-art deep learning techniques, including hierarchical and flat models, with attention mechanisms.
result Best model achieves 90.3% accuracy on topography site assignment and 84.8% on morphology type assignment.
System detects and classifies skin cancer using CNN.
problem Early detection and classification of skin cancer is challenging and costly.
method Used MNIST HAM-10000 dataset, image processing, deep learning (CNN), and transfer learning (ResNet).
result CNN model achieved 0.88 precision, 0.74 recall, and 0.77 f1-score.
AI enhances cancer diagnostics using spectroscopy.
problem Early and accurate cancer diagnosis.
method Combining AI with spectroscopy-based techniques.
result AI improves cancer diagnostics speed and safety.
Study uses NMF to reduce cancer microarray data dimensions.
problem High dimensionality of cancer microarray data hinders understanding.
method Used Non-negative Matrix Factorization (NMF) for dimensionality reduction.
result NMF achieves 98% classification accuracy.
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell …
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.
The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more…
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.
In this paper, we analyze the Wisconsin Diagnostic Breast Cancer Data using Machine Learning classification techniques, such as the SVM, Bayesian Logistic Regression (Variational Approximation), and K-Nearest-Neighbors. We describe each model, and compare their performance through different measures. We conclude that S…
Machine learning classifies colorectal tissue using photoacoustic microscopy.
problem Traditional diagnostic methods for colorectal cancer are limited in detail and painful.
method Machine learning applied to acoustic resolution photoacoustic microscopy.
result Machine learning accurately classified benign and malignant tissue.
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.
The article examines different thresholding methods for improving PAM algorithm in cancer classification.
problem High-dimensional classification with too many features selected by PAM.
method Extends PAM with hard and order thresholding methods and a deep search algorithm.
result Improved cancer status prediction accuracy and smaller number of features.
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.
Microarray cancer gene expression data comprise of very high dimensions. Reducing the dimensions helps in improving the overall analysis and classification performance. We propose two hybrid techniques, Biogeography - based Optimization - Random Forests (BBO - RF) and BBO - SVM (Support Vector Machines) with gene ranki…
Robust method estimates self-similarity for mammogram images, improving cancer detection.
problem Statistical assessment of self-similarity in real data with large mean level shifts.
method Theil-type weighted regression for wavelet-based estimation, compared to OLS and AV.
result Robust approach shows nearly 68% accuracy in cancer vs non-cancer classification.
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.
CAT framework improves AI medical screening fairness and reliability.
problem Imbalanced data, varying performance across cohorts, and patient-level inconsistencies in traditional metrics.
method CAT framework introduces patient-level assessment, entropy-based distribution weighting, and cohort-weighted sensitivity and specificity.
result Enhanced predictive reliability, fairness, and interpretability of AI-driven medical screening models.
Deep learning models outperform dermatologists in skin cancer classification.
problem Skin cancer classification using dermoscopy images.
method Different pre-trained deep learning architectures (DenseNet 201, ResNet 152, Inception v3, InceptionResNet v2) applied on dermoscopy images.
result DenseNet 201 achieved highest AUC values for melanoma and basal cell carcinoma.
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 …
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.
A novel two-stage resampling method improves CNN training on imbalanced colorectal cancer image data.
problem Data imbalance in medical image datasets, especially in histopathological images.
method Two-stage resampling: first oversampling in image space, then undersampling in feature space.
result The proposed method enhances CNN training on imbalanced colorectal cancer image datasets.
Exponentially fast SMF algorithm for multi-class classification.
problem Learning interpretable features from high-dimensional data.
method Novel framework that 'lifts' SMF as a low-rank matrix estimation problem.
result Provable exponential convergence to global minimizer under mild assumptions.
Breast density classification is an essential part of breast cancer screening. Although a lot of prior work considered this problem as a task for learning algorithms, to our knowledge, all of them used small and not clinically realistic data both for training and evaluation of their models. In this work, we explore the…
Model patching closes subgroup performance gaps in skin cancer classification.
problem Inconsistent model performance on specific subgroups of a class.
method Two-stage framework that models subgroup features and learns semantic transformations, followed by data augmentation.
result Reductions in robust error of up to 33% relative to best baseline on benchmark datasets.