CytoData2023, October 23-26, includes the Symposium (workshops, presentations, and a poster session) and the Hackathon. The Symposium is scheduled for the first two days whereas the Hackathon happens during the last two days of the CytoData2023. The schedule can be seen below.
Monday, 23 October 2023

Location: University of Helsinki main building (Unioninkatu 34)

12:30 - 18:00 Registration

13:00 - 15:00 Workshops 1 & 2
Recursion: Biological Cartography: Building and Benchmarking Representations of Life
ThinkCyte: TBA

15:00 - 15:30 Coffee Break

15:30 - 17:30 Workshops 3 & 4
Calico: TBA
TissueGnostics: TBA

17:30 - 18:00 Opening of the Symposium

18:00 - 19:00 Opening Keynote: Berend Snijder, ETH Zurich: Image-based drug screening in human tissues for precision oncology

Tuesday, 24 October 2023

Location: University of Helsinki main building (Unioninkatu 34)

09:00 - 10:30 Session 1: Spatial profiling
09:00 - 09:45 Invited: Leeat Keren, Weizmann Institute of Science: Unraveling the tumor microenvironment by multiplexed imaging
09:45 - 10:00 Loan Vulliard, German Cancer Research Center: Integrating multiplexed imaging data with explainable panel-independent cellular profiling
10:00 - 10:15 Jovan Tanevski, Heidelberg University and Heidelberg University Hospital: Explainable models for global and local representation of spatial data
10:15 - 10:30 Yael Amitay, Weizmann Institute of Science: CellSighter: a neural network to classify cells in highly multiplexed images

10:30 - 11:00 Coffee Break

11:00 - 12:30 Session 2: Methods
11:00 - 11:45 Invited: Wei Ouyang, KTH Royal Institute of Technology: Web and cloud infrastructure for AI-powered bioimage analysis
11:45 - 12:00 Vivian Lu, Deepcell, Inc.: Self-supervised Foundation Model Captures High-dimensional Morphology Data from Single Cell Brightfield Images
12:00 - 12:15 Mohammad Vali Sanian, Institute for Molecular Medicine Finland (FIMM): Decoupled Self-Supervised Training of Encoder-Decoder Transformers
12:15 - 12:30 Benjamin Morris, Allen Institute for Cell Science: CytoDL: a comprehensive deep-learning tool for streamlining microscopy image transformations

12:30 - 13:30 Lunch Break

13:30 - 15:00 Session 3: Cell Painting and Datasets
13:30 - 14:15 Invited: Anne Carpenter, Broad Institute: Cell Painting: a decade of image-based profiling
14:15 - 14:30 Jesko Wagner, University of Edinburgh: Unlocking Single-Cell Morphological Profiling
14:30 - 14:45 Christopher Schmied, Leibniz-Forschungsinstitut für Molekulare Pharmakologie (FMP) Campus Berlin: Morphological profiling dataset based on the EU-OPENSCREEN bioactive library over two cell lines
14:45 - 15:00 Adam Taylor, Sage Bionetworks: Advancing FAIR data sharing for multiplexed tissue imaging data from the Human Tumor Atlas Network

15:00 - 15:30 Coffee Break

15:30 - 16:45 Session 4: Single-cell profiling
15:30 - 16:15 Invited: Loïc A. Royer, Chan Zuckerberg Biohub: Self-supervised deep learning encodes high-resolution features of protein subcellular localization
16:15 - 16:30 Javed Iqbal, German Cancer Research Center: A global genetic interaction network by single-cell image analysis and machine learning
16:30 - 16:45 Nikita Moshkov, Biological Research Centre (BRC): Unbiased single-cell morphology with self-supervised vision transformers

16:45 - 18:00 Poster Session

19:00 - 20:00 Reception hosted by the City of Helsinki (address: Pohjoisesplanadi 11-13)

Poster presentations:
Emilia Piki, University of Oulu: ROR1-STAT3 signaling contributes to ovarian cancer intra-tumor heterogeneity
Dominik Hirling, Biological Research Centre (BRC): Segmentation Metric Misinterpretations in Bioimage Analysis
Veera Timonen, Institute for Molecular Medicine Finland (FIMM), University of Helsinki: Learning single-cell high-content phenotypes and their genetic determinants in healthy blood donors
Irina Belaia, Turku BioImaging, Åbo Akademi University and University of Turku: Turku BioImaging image data services
Joanna Pylvänäinen, Åbo Akademi University: Empowering insights into cancer metastasis: Advanced image analysis approaches for live cell imaging
Dimitri Meistermann, University of Helsinki: Cell painting at single-cell level for characterization of Cortical Neuron Development in vitro
Myriam Sevigny, University of Helsinki: Genome Biology Unit (GBU) - digital scanning at your service!
Junel Solis, Turku BioImaging, Åbo Akademi University and University of Turku: Mouse Brain Alignment Tool: Software for autoradiography images
Zsanett Zsófia Iván, Biological Research Centre: The proteomic map of mitosis
Chun Hao Wong, Wellcome Sanger Institute: Optimising direct RNA in situ sequencing for optical pooled CRISPR screens
Lakshmi Balasubramanian, Centre for Cellular and Molecular Platforms: A Constructive Approach of image-based profiling of single cells based on Image Flow Cytometry Data
Fred Mast, Seattle Children's Research Institute: A graph convolutional neural network to decipher signal cascades controlling peroxisome biogenesis
Martina K. Zowada, German Cancer Research Center (DKFZ): High-throughput AI-based phenotypic screening for drug discovery
Zitong Chen, Broad Institute of MIT and Harvard: CHAMMI: A benchmark for channel-adaptive models in microscopy imaging
Sonja Koivukoski,  University of Eastern Finland: AI-driven Virtual Histological Staining
Dado Tokic, Turku BioImaging, University of Turku & Åbo Akademi University: Collaboration between academia and industry in image analysis offers novel opportunities for both
Christa Ringers, Uppsala University: Morphological profiling by Cell Painting to capture colorectal cancer disease heterogeneity
Minttu Polso, Institute for Molecular Medicine Finland (FIMM), University of Helsinki: Image-based and machine learning-guided multiplexed serology test for SARS-CoV-2
Erik Serrano, University of Colorado Anschutz Medical Campus: CytoSnake: Orchestrating reproducible workflows with PyCytominer
Isabel Cristina, FIMM, University of Helsinki: AI-driven phenotypic image based interaction profiling for functional cancer precision medicine
Antti Kiviaho, Tampere University and Tays Cancer Center: Image-based classification of uterine leiomyoma driver mutations using deep learning
Taras Redchuk, University of Helsinki: Chromatin dynamics in serum starvation: machine learning-assisted analysis of tracking data
Szymon Adamski, Ardigen: AI-driven identification of hits from Cell Painting based screening

Wednesday, 25 October 2023

Location: Biomedicum (Haartmaninkatu 8)

09:00 - 10:30 Introduction to the Hackathon

10:30 - 13:00 Hackathon

13:00 - 14:00 Lunch Break

14:00 - 18:00 Hackathon

18:00 - Social Event

Thursday, 26 October 2023

Location: Biomedicum (Haartmaninkatu 8)

09:00 - 13:00 Hackathon

13:00 - 14:00 Lunch Break

14:00 - 15:00 Wrapping up the Hackathon