# Addressing persistent challenges in digital image analysis of cancer tissue: resources developed from a hackathon

## TL;DR

Researchers collaborated in a virtual event to develop tools for analyzing complex cancer tissue images and shared the resulting resources.

## Contribution

A collaborative hackathon produced publicly available resources to address challenges in digital cancer image analysis.

## Key findings

- Participants developed solutions for cell type classification and spatial data visualization.
- Resources were created to handle artifacts and improve image representation learning.
- The hackathon highlighted remaining challenges in scaling analysis for large datasets.

## Abstract

The National Cancer Institute (NCI) supports numerous research consortia that rely on imaging technologies to study cancerous tissues. To foster collaboration and innovation in this field, the Image Analysis Working Group (IAWG) was created in 2019. As multiplexed imaging techniques grow in scale and complexity, more advanced computational methods are required beyond traditional approaches like segmentation and pixel intensity quantification. In 2022, the IAWG held a virtual hackathon focused on addressing challenges in analyzing complex, high‐dimensional datasets from fixed cancer tissues. The hackathon addressed key challenges in three areas: (1) cell type classification and assessment, (2) spatial data visualization and translation, and (3) scaling image analysis for large, multi‐terabyte datasets. Participants explored the limitations of current automated analysis tools, developed potential solutions, and made significant progress during the hackathon. Here we provide a summary of the efforts and resultant resources and highlight remaining challenges facing the research community as emerging technologies are integrated into diverse imaging modalities and data analysis platforms.

Large multidimensional digital images of cancer tissue are becoming prolific, but many challenges exist to automatically extract relevant information from them using computational tools. We describe publicly available resources that have been developed jointly by expert and non‐expert computational biologists working together during a virtual hackathon to address several of these challenges, including artifact handling and image representation learning.