arXiv subjects

Amir Reza Vazifeh

Publications and source records attributed to Amir Reza Vazifeh.

2 recordsLinked to original sources

arXiv2026

Label-free cell counting and viability prediction with brightfield imaging and deep learning

Cell viability assessment is a core requirement in cell culture systems, with critical applications in biopharmaceutical manufacturing and drug development. Conventionally, it is measured by adding membrane-impermeable dyes to a sample (a process called staining), which allows compromised cell membranes to be distinguished from intact ones. However, staining has several limitations: (a) chemical agents can perturb normal cellular processes of the cells being measured, (b) it is often ambiguous to assign viability to individual cells whose membrane integrity is only partially compromised. (c) photobleaching can undermine measurement accuracy over time when using fluorescent stains, and (d) staining cannot be performed in situ or in real time. Here, we show that (1) stained cells captured under brightfield imaging contain sufficient information to distinguish live and dead cells, and (2) cells captured under unstained brightfield imaging exhibit similar image features to their stained counterparts, enabling models trained on stained cells to generalize to unstained ones. We then report the development and validation of ViabiLens, an AI-assisted software for label-free cell viability analysis. The ViabiLens combines a cell detection model for localizing individual cells with a convolutional neural network (CNN) classifier for live/dead prediction, paired with an interactive UMAP-based viewer for visualizing and exploring individual cells across the sample. Evaluated on Chinese Hamster Ovary (CHO) cells spanning a wide range of viability conditions, ViabiLens achieves a mean absolute error of 2.68\% on unstained samples against fluorescence-based reference measurements. We also release a benchmark dataset for label-free cell viability analysis to facilitate future research, available at https://amirrezavazifeh.github.io/ViabiLens-Project-Page/.

cs.CV↗

arXiv2026

Manifold Learning for Personalized and Label-Free Detection of Cardiac Arrhythmias

Electrocardiograms (ECGs) provide non-invasive measurements of heart activity and are established tools for detecting cardiac arrhythmias. Although supervised machine learning has emerged as a promising approach for automated heartbeat classification, substantial variations in ECG signals across individuals and leads, combined with inconsistent labeling standards and dataset biases, make it difficult to develop generalizable models. Dimensionality reduction maps high-dimensional data into a lower-dimensional space while preserving the underlying structure, enabling visualization and pattern discovery. Conventional methods, e.g., principal component analysis, prioritize large variances and typically overlook subtle yet clinically relevant patterns. Here, we show that nonlinear dimensionality reduction (NLDR) algorithms, e.g., t-SNE and UMAP, can identify medically relevant features in ECG signals without pretraining or prior information. Using the MIT-BIH Arrhythmia Database, we show that: a) applying NLDR to a mixed population of heartbeats reveals inter-individual morphological differences, as signals from the same person cluster together in latent spaces; and b) applying NLDR to heartbeats of a single individual separates normal beats from arrhythmias into distinct clusters, identifiable in an unsupervised manner. To our knowledge, this is the first systematic evaluation of NLDR for unsupervised arrhythmia detection. Both UMAP and t-SNE achieved trustworthiness scores >=0.95, indicating that local neighborhoods are well preserved in the embedding. Classification on 2D embeddings outperforms the original high-dimensional space, with a k-NN classifier discriminating individual recordings with >=80% accuracy and identifying arrhythmias with median accuracy >=98% and median F1-score >=85%. These results show that NLDR holds much promise for cardiac monitoring and personalized healthcare.

cs.LG↗