arXiv:2603. 13377v2 Announce Type: replace-cross Abstract: Representation learning has driven major advances in natural image analysis by enabling models to acquire high-level semantic features.
arXiv:2606. 05139v1 Announce Type: new Abstract: The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets.
The study evaluates self‑supervised learning (SSL) models pretrained on ImageNet‑1k and the Human Protein Atlas (HPA) Field‑of‑View (FOV) for protein localization in microscopy images. DINO‑based Vision Transformer backbones pretrained on either dataset transfer well to the OpenCell dataset, achieving strong performance even without fine‑tuning and improving further when fine‑tuned (0.704 ± 0.027 macro F1 on 17 classes). At the single‑cell level, the HPA‑pretrained model outperforms others in k‑nearest‑neighbor classification across all neighborhood sizes (macro F1 ≥ 0.515).
arXiv:2608. 14293v1 Announce Type: cross Abstract: High-content microscopy enables systematic profiling of cellular responses to chemical perturbations, but the scale of the chemical space makes exhaustive phenotypic characterization experimentally infeasible.
arXiv:2507. 04704v3 Announce Type: replace-cross Abstract: Understanding how cellular morphology, gene expression, and spatial context jointly shape tissue function is a central challenge in biology.
arXiv:2608. 10657v1 Announce Type: cross Abstract: Leukemia cell image classification is challenged by real-world domain shifts from acquisition, staining, illumination, and site protocols, causing single-dataset models to generalize poorly in real clinical scenarios.
arXiv:2607. 22712v1 Announce Type: cross Abstract: Single-cell light microscopy images have become an important data source for characterizing cell phenotypes, but their complexity and heterogeneity pose challenges to high-throughput automated analysis.
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
arXiv:2508. 07345v2 Announce Type: replace-cross Abstract: \textbf{Introduction:} Accurate prediction of Phage Virion Proteins (PVP) is essential for genomic studies due to their crucial role as structural elements in bacteriophages.
arXiv:2607. 18218v1 Announce Type: cross Abstract: Foundation models have emerged as a driving force in computational pathology, with the potential to transform cancer diagnosis, prognosis, and treatment selection by learning transferable representations from large-scale histopathology data.
arXiv:2509.23552v2 Announce Type: replace-cross Abstract: Antimicrobial Resistance (AMR) is a rapidly escalating global health crisis. While genomic sequencing enables rapid prediction of resistance...
arXiv:2608. 14710v1 Announce Type: cross Abstract: Predicting spatial gene expression from hematoxylin and eosin (H\&E)-stained images offers a cost-effective alternative to spatial transcriptomics (ST).