Cell detection and cell type classification from biomedical images play an important role for high-throughput imaging and various clinical application. While classification of single cell sample can be performed with standard computer vision and machine learning methods, analysis of multi-label samples (region containi…
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48 results for “cell classification”
Proposes a model for identifying 4G cells with network throughput problems.
problem Challenges in identifying 4G cells with network throughput issues due to network complexity and privacy concerns.
method Data-driven model using clustering and Deep Neural Networks (DNNs). Model parameters are learned from a small number of expert-labeled data. Multiple clustering models capture common features for problematic cells.
result The proposed model outperforms a simple classifier in identifying cells with network throughput problems.
New method classifies reticulocytes from red blood cells without labels.
problem Classifying reticulocytes from mature red blood cells without fluorescent labels.
method Unsupervised machine learning on morpho-rheological markers.
result Promising results in classifying reticulocytes from mature red blood cells.
Multi-Class Classification of Blood Cells -- End to End Computer Vision based diagnosis case studycs.CV
This study automates blood cell classification using computer vision.
problem Automated detection and classification of white blood cells.
method Color-based segmentation, morphological processing, feature extraction, PCA, Unsupervised and Supervised learning algorithms, Deep Convolutional Neural Networks.
result Identification of robust algorithms for automated blood cell classification.
Matching cells over time has long been the most difficult step in cell tracking. In this paper, we approach this problem by recasting it as a classification problem. We construct a feature set for each cell, and compute a feature difference vector between a cell in the current frame and a cell in a previous frame. Then…
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…
We propose a new deep learning approach for medical imaging that copes with the problem of a small training set, the main bottleneck of deep learning, and apply it for classification of healthy and cancer cells acquired by quantitative phase imaging. The proposed method, called transferring of pre-trained generative ad…
TIMELY improves consistency in labeling blood cell images.
problem Inconsistent labeling of blood cells in microscopy images leads to unreliable diagnoses.
method TIMELY combines pseudotime inference and hidden Markov trees to correct labeling mistakes.
result TIMELY outperforms baseline methods in identifying and correcting inconsistent labels.
Understanding the morphological changes of primary neuronal cells induced by chemical compounds is essential for drug discovery. Using the data from a single high-throughput imaging assay, a classification model for predicting the biological activity of candidate compounds was introduced. The image recognition model wh…
New model generates realistic single-cell gene expression data.
problem Generating realistic single-cell gene expression profiles is challenging.
method scLDM, a latent diffusion model using Diffusion Transformers and linear interpolants.
result Superior performance in generating realistic single-cell gene expression data.
Computational ghost imaging is an imaging technique in which an object is imaged from light collected using a single-pixel detector with no spatial resolution. Recently, ghost cytometry has been proposed for a high-speed cell-classification method that involves ghost imaging and machine learning in flow cytometry. Ghos…
Counting and classifying blood cells is an important diagnostic tool in medicine. Support Vector Machines are increasingly popular and efficient and could replace artificial neural network systems. Here a method to classify blood cells is proposed using SVM. A set of statistics on images are implemented in C++. The MPE…
C2G-Net improves image classification of similar objects like cells.
problem Classifying images with many similar objects efficiently and interpretably.
method Combines image compression and a CNN with reduced parameters.
result C2G-Net achieves similar accuracy to conventional CNNs but with reduced training time and improved interpretability.
Donor-aware scRNA-seq benchmarks improve classification accuracy in inflammatory bowel disease.
problem Influenza disease classification from scRNA-seq data is prone to donor-level confounding.
method Developed and evaluated three feature representations across two IBD cohorts.
result Compartment-stratified CLR composition and GatedStructuralCFN embeddings outperform linear models in classification accuracy.
New metric scores perturbations across populations, not cells, improving model comparison.
problem Single-cell perturbation data overlaps, making per-cell accuracy unreliable.
method Average per-cell probability vectors over all cells of a perturbation to form a population profile and rank candidate perturbations.
result Classifier Discrimination Score (CDS) identifies true perturbation more reliably than pseudobulk-based scores.
HSSE framework embeds single-cell RNA-seq data at multiple scales.
problem Capturing heterogeneous local structure in single-cell RNA-seq data.
method Hierarchical sheaf spectral embedding (HSSE) framework.
result HSSE achieves competitive or improved performance in single-cell RNA-seq data representation learning.
Most of the banks' operational risk internal models are based on loss pooling in risk and business line categories. The parameters and outputs of operational risk models are sensitive to the pooling of the data and the choice of the risk classification. In a simple model, we establish the link between the number of ris…
Understanding cell identity is an important task in many biomedical areas. Expression patterns of specific marker genes have been used to characterize some limited cell types, but exclusive markers are not available for many cell types. A second approach is to use machine learning to discriminate cell types based on th…
Energy-efficient detection of natural errors in deep networks.
problem Deep networks lack error detection capability without additional energy costs.
method Append RACs at hidden layers to detect natural errors with early classification termination.
result Early classification termination reduces energy consumption.
Determining the localization of specific protein in human cells is important for understanding cellular functions and biological processes of underlying diseases. Among imaging techniques, high-throughput fluorescence microscopy imaging is an efficient biotechnology to stain the protein of interest in a cell. In this w…
Few-shot cell segmentation from diverse sources to target domain.
problem Efficient cell segmentation from limited annotated images.
method Meta-learning combining cross-domain tasks and invariant representation.
result Promising results from 1-10-shot learning on public databases.
New system constructs cell-type taxonomy across multiple samples.
problem Challenges in matching clusters from different datasets.
method Combines Optimal Transport with Relaxed Marginal Constraints (OT-RMC) for simultaneous alignment of clusters across multiple samples.
result Highly accurate annotation of cell types and sample-level feature extraction.
Mobile Traffic Classification through Physical Channel Fingerprinting: a Deep Learning Approacheess.SP
The automatic classification of applications and services is an invaluable feature for new generation mobile networks. Here, we propose and validate algorithms to perform this task, at runtime, from the raw physical channel of an operative mobile network, without having to decode and/or decrypt the transmitted flows. T…
DISPR uses diffusion models to predict 3D cell shapes from 2D images.
problem Predicting 3D cell shapes from 2D microscopy images.
method Diffusion model trained to predict 3D shapes from 2D microscopy images as a prior.
result Adding DISPR predictions to minority cell classes improves classification accuracy.
Understanding if classifiers generalize to out-of-sample datasets is a central problem in machine learning. Microscopy images provide a standardized way to measure the generalization capacity of image classifiers, as we can image the same classes of objects under increasingly divergent, but controlled factors of variat…
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 sets of observations, where each set is labeled with class information, and the prediction of a class label is performed a…
There are various algorithms and methodologies used for automated screening of cervical cancer by segmenting and classifying cervical cancer cells into different categories. This study presents a critical review of different research papers published that integrated AI methods in screening cervical cancer via different…
PDE-Foam - a probability-density estimation method using self-adapting phase-space binningphysics.data-an
Probability Density Estimation (PDE) is a multivariate discrimination technique based on sampling signal and background densities defined by event samples from data or Monte-Carlo (MC) simulations in a multi-dimensional phase space. In this paper, we present a modification of the PDE method that uses a self-adapting bi…
Extracting an understanding of the underlying system from high dimensional data is a growing problem in science. Discovering informative and meaningful features is crucial for clustering, classification, and low dimensional data embedding. Here we propose to construct features based on their ability to discriminate bet…
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…
LOT framework embeds high-dimensional cell data into interpretable Euclidean space.
problem Lack of interpretable methods for high-dimensional cell data.
method Adapts Linear Optimal Transport (LOT) to irregular point clouds.
result Accurate and interpretable classification and synthetic data generation.
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 …
DCENWCNet improves WBC classification with LIME-based explainability.
problem Classifying white blood cells from blood images with high accuracy and interpretability.
method Integrates three CNN architectures with different settings to balance feature learning and performance.
result Achieves highest mean accuracy on the Rabbin-WBC dataset, outperforming state-of-the-art networks.
Bivariate Beta-LSTMcs.LG
Long Short-Term Memory (LSTM) infers the long term dependency through a cell state maintained by the input and the forget gate structures, which models a gate output as a value in [0,1] through a sigmoid function. However, due to the graduality of the sigmoid function, the sigmoid gate is not flexible in representing m…
Method learns cell interaction rules from individual trajectories.
problem Inferring interaction rules from heterogeneous cellular data.
method WSINDy for second order IPSs, learning individual cell models.
result Efficiently identifies different species and best-fit models for each.
ABO3 Perovskites' Formability Prediction and Crystal Structure Classification using Machine Learningcond-mat.mtrl-sci
Machine learning predicts perovskite formability and classifies crystal structures.
problem Predicting and classifying perovskite formability and crystal structures.
method Machine learning, specifically Random Forest, with 5-fold cross-validation.
result 98.57% accuracy in predicting perovskite formability and 90.53% in classifying crystal structures.
Generates infinite-depth hierarchical clusters from few examples.
problem Inadequate finite-sample clustering methods for fine-scale hierarchical structures.
method Classification fields generated by a local refinement rule, approximated by predictors.
result Learned predictors can approximate infinite-depth hierarchical structures.
SOCP uses SOM to find groups and local calibration buffers for better regional coverage.
problem Heterogeneous regional coverage gaps in conformal prediction.
method Self-Organizing Map (SOM) for group discovery; local calibration buffers at BMU or fixed grid.
result Reduces regional coverage gaps on 7/8 benchmarks by 7.1%.
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, Inception v3, InceptionResNet v2) were used and applied on 10135 dermoscopy skin images in total (H…
We consider the use of the Joint Clustering and Matching (JCM) procedure for the supervised classification of a flow cytometric sample with respect to a number of predefined classes of such samples. The JCM procedure has been proposed as a method for the unsupervised classification of cells within a sample into a numbe…
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 …
Resource is an important constraint when deploying Deep Neural Networks (DNNs) on mobile and edge devices. Existing works commonly adopt the cell-based search approach, which limits the flexibility of network patterns in learned cell structures. Moreover, due to the topology-agnostic nature of existing works, including…
Clouds frequently cover the Earth's surface and pose an omnipresent challenge to optical Earth observation methods. The vast majority of remote sensing approaches either selectively choose single cloud-free observations or employ a pre-classification strategy to identify and mask cloudy pixels. We follow a different st…
Kernel testing compares cell states in single-cell data.
problem Comparing non-linear cell states in single-cell data.
method Kernel-based testing framework for non-linear distribution comparison.
result Identifies subtle population variations in cell states.
Cellular Tree Classifiersstat.ML
The cellular tree classifier model addresses a fundamental problem in the design of classifiers for a parallel or distributed computing world: Given a data set, is it sufficient to apply a majority rule for classification, or shall one split the data into two or more parts and send each part to a potentially different …
Forest Fire Clustering discovers cell types from single-cell data.
problem Discovering cell types from large-scale single-cell sequencing data.
method Iterative label propagation and parallelized Monte Carlo simulation.
result Forest Fire Clustering outperforms state-of-the-art methods on diverse benchmarks.
Proposes CCCVAE for better single-cell clustering with cell-cell communication.
problem Improving single-cell RNA sequencing clustering by incorporating cell-cell communication.
method Integrates cell-cell communication into a variational autoencoder framework.
result Empirical results show CCCVAE outperforms standard VAEs in clustering performance.
Improved GPLVM model for single-cell RNA-seq data.
problem Lack of effective scalable models for clustering cell types in large-scale single-cell RNA-seq data.
method Introduces amortized stochastic variational Bayesian GPLVM (BGPLVM) tailored for single-cell RNA-seq.
result Matches the performance of scVI on synthetic and real-world datasets and reveals more interpretable latent structures.