Author

Amirhossein Vahidi

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Open access Oct 2026

Fine-Grained and Reliable Cell Classification from Pathology Images

Molecular assays such as spatial transcriptomics facilitate fine-grained identification of cell types for analyzing tumor microenvironments. However, they are expensive to acquire in routine clinical practice compared to hematoxylin and eosin (H&E) images. With molecular guidance, such images can distinguish cell linea...

Sevda Öğüt, Arpit Merchant, Amirhossein Vahidi et al. · bioRxiv · 0 citations

#machine learning Preprint Oct 2026

ORCA: Hunting Compositional Failures in Text-to-Image Diffusion

Text-to-image diffusion models fail predictably on compositional prompts: attributes bind to the wrong objects, spatial relations invert, and multi-object scenes lose count. Recent architectures already augment CLIP with a T5 encoder precisely because CLIP's contrastive embedding loses compositional structure, yet thes...

Arshia Hemmat, Amirhossein Vahidi, Amitis Shidani et al. · 0 citations

Open access Aug 2026

Multi-scale modeling of human tissues from spatial transcriptomics with TERRA

Spatial transcriptomics maps gene expression at cellular resolution, revealing how cells organize into multicellular niches. Yet computational analyses remain dataset-specific, without a transferable representation of tissue organization that generalizes across datasets, tasks and tissues or predicts how tissues behave...

Sebastian Birk, M. V. Sanian, Amirhossein Vahidi et al. · bioRxiv · 4 citations · ⚡1

Open access Aug 2026

Learning Discrete Cell and Niche Codes from Spatial Transcriptomics Using Dual Residual Vector Quantization

SQUINT is presented, a graph vector-quantized variational autoencoder that learns two disjoint codebooks per cell from a shared architecture that outperforms or is competitive with strong baselines on identification and achieves the most faithful cross-section integration.

Sebastian Birk, Arpit Merchant, Amirhossein Vahidi et al. · bioRxiv · 0 citations