Albert Dominguez Mantes

PhD Student

École Polytechnique Fédérale de Lausanne (EPFL)

Hey there! I’m a PhD student in the Weigert group and the La Manno group at EPFL in Switzerland. I hold a BSc in Data Science and Engineering by the Universitat Politècnica de Catalunya (UPC), and I’m currently enrolled in the Computational Biology PhD program at EPFL.

I’m developing computational methods for the analysis of fluorescence microscopy images. One of my main focus is on developing methods that are easily adoptable for other researchers. As such, I’m big on open source and best software engineering practices.

Previously, I worked on ML-based methods for population genetics in the Bustamante lab at Stanford University, in collaboration with the Image Processing group at Universitat Politècnica de Catalunya (UPC).

Outside of academia, I spent two great years working as a Data Scientist at a startup, Abi Global Health, where I developed and deployed NLP models for usage in a conversational setting. I was also involved in maintaining the main product’s backend as well as general analytics.

Please reach out if you’re interested in my work or just want to chat about any of these topics!

Selected publications

MuViT: Multi-Resolution Vision Transformers for Learning Across Scales in Microscopy

Albert Dominguez Mantes , Gioele La Manno , and Martin Weigert

Conference on Computer Vision and Pattern Recognition (CVPR), 2026

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Modern microscopy routinely produces gigapixel images that contain structures across multiple spatial scales, from fine cellular morphology to broader tissue organization. Many analysis tasks require combining these scales, yet most vision models operate at a single resolution or derive multi-scale features from one view, limiting their ability to exploit the inherently multi-resolution nature of microscopy data. We introduce MuViT, a transformer architecture built to fuse true multi-resolution observations from the same underlying image. MuViT embeds all patches into a shared world-coordinate system and extends rotary positional embeddings to these coordinates, enabling attention to integrate wide-field context with high-resolution detail within a single encoder. Across synthetic benchmarks, kidney histopathology, and high-resolution mouse-brain microscopy, MuViT delivers consistent improvements over strong ViT and CNN baselines. Multi-resolution MAE pretraining further produces scale-consistent representations that enhance downstream tasks. These results demonstrate that explicit world-coordinate modelling provides a simple yet powerful mechanism for leveraging multi-resolution information in large-scale microscopy analysis.
Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression

Albert Dominguez Mantes , Antonio Herrera , Irina Khven , and 14 more authors

Nature Methods, 2025

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Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow.