About VIPER-UNIGE

VIPER stands for Visual Information Processing for Eenhanced Retrieval, and it is an academic group dealing with the development of Machine Learning and Data Mining techniques. Our current research interests include:

  • Large-scale and High-dimensional issues in Machine Learning (e.g., Manifold Learning and Approximate Indexing);
  • Large-scale Data Mining (Big Data);
  • Large-scale Information Indexing (Distributed Indexing).

We collaborate closely with the Data Mining and Machine Learning (DMML) group of the University of Applied Sciences, Western Switzerland (HEG), and the MedGIFT group from the University of Applied Sciences, Sierre, Wallis (HEVs). Take a look at our benchmark study carried out in partnership with the Swiss National Science Foundation (grant number 207509 ”Structural Intrinsic Dimensionality"), and the Geneva University Hospital (HUG)!

Latest Work

FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking

Authors: Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou, Thomas Matthes, Stéphane Marchand-Maillet

 arXiv    Paper   Code    Cite    Data

Abstract: This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30 patients, with each cell characterized by twelve markers. Ground truth labels identify five hematological cell types: T lymphocytes, B lymphocytes, Monocytes, Mast cells, and Hematopoietic Stem/Progenitor Cells (HSPCs). Experiments utilize supervised inductive learning and semi-supervised transductive learning on up to 1 million cells per patient. Baseline methods include Gaussian Mixture Models, XGBoost, Random Forests, Deep Neural Networks, and Graph Neural Networks (GNNs). GNNs demonstrate superior performance by exploiting spatial relationships in graph-encoded data. The benchmark allows standardized evaluation of clinically relevant classification tasks, along with exploratory analyses to gain insights into hematological cell phenotypes. This represents the first public flow cytometry benchmark with a richly annotated, heterogeneous dataset. It will empower the development and rigorous assessment of novel methodologies for single-cell analysis.

Selected Publications

See our publication list for more complete reports.

Academic Info

The Viper group is one of the research groups of the Computer Science Center (CUI) of the University of Geneva.