> For the complete documentation index, see [llms.txt](https://www.parapathology.com/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://www.parapathology.com/appendix/clippings/solving-unpopular-problems-the-qupath-story-the-pathologist.md).

# Solving Unpopular Problems: The QuPath Story

Meet the developer of the open-source digital pathology platform that's transforming image analysis across the globe. An interview with QuPath creator Peter Bankhead (University of Edinburgh) on the s

### Summary

An interview (by Helen Bristow, *The Pathologist*, 29 July 2026) with **Peter Bankhead**, Reader at the Institute of Genetics and Cancer, University of Edinburgh, and creator of **QuPath** — one of the world's most widely used open-source image-analysis platforms for digital pathology research. The QuPath team was recently given a Royal College of Pathologists team award for innovation. The piece frames QuPath's success against a road "beset with false starts, administrative battles, rejection, and frustration."

> "When I started in digital pathology, I felt the lack of a pathology-friendly open-source platform was a big problem." — Peter Bankhead

### Origins — an accidental platform

* Bankhead entered digital pathology as a postdoc in **2012**. His background was retinal image analysis (PhD) and three years as an image analyst in a microscopy facility; he hadn't appreciated how large and complex whole-slide images could be, and existing tools weren't designed for them.
* He spent \~2 years trying to adapt image-analysis tools to **score IHC biomarkers in tissue microarrays** — largely unsuccessful because the plugins/scripts he wrote were hard to use.
* He eventually set out to write a whole-slide image viewer "mostly to prove to myself it wouldn't work." With the help of **OpenSlide** (an open-source library for reading pathology image formats), the viewer quickly became useful; adding analysis features on top of it grew into QuPath.

> "It wasn't planned, and it only happened because not writing QuPath hadn't worked very well."

### Setbacks

* Bankhead had to **fight to make QuPath open-source** — permission to release it was granted only after he resigned his postdoc, during his notice period. When a later job stopped him continuing the work (even in his spare time), he left that too, and spent some months unemployed.
* The main QuPath paper was **rejected by at least five journals** without being sent for peer review, considered unlikely to have much impact. These years, though exhausting, made him less concerned with conventional measures of success.

### Reach and impact

* The main paper was eventually published in **Scientific Reports** and has been cited **>6,000 times** (>1,500 new citations last year alone) — an undercount, since many studies use QuPath without citing it.
* The software has been **downloaded over a million times**, used across academia and industry and worldwide across many diseases; one major tech company even featured QuPath in product launch videos.

### How it is used

* Many projects involve **detecting and classifying cells** in WSIs by morphology, staining, or both. Its application to **Ki67** is well established (identify tumour vs non-tumour cells by morphology, then compute the % of tumour cells positive by staining).
* Crucially, QuPath has **no dedicated Ki67 algorithm** — it provides image-processing and machine-learning **building blocks** for custom algorithms, which is what makes it flexible. It's not limited to cells or to WSIs (projects include fluorescence confocal z-stacks and electron microscopy). Bankhead has personally used it to digitise a family photo album; he's heard of it being used on fossils and to quantify the fracture behaviour of cheese.

### Community and ecosystem

* A small core team (never more than a few people) with a much larger user community — tens of thousands of posts across **>5,500 topics** on the Scientific Community Image Forum.
* The team keeps writing the core software themselves and encourages others to build **extensions** rather than fork:
  * Collaboration with **Joel Saltz's group at Stony Brook** (esp. Jakub Kaczmarzyk) to run AI models interactively.
  * An extension for **InstanSeg**, a fast, accurate AI model for nuclei/cell detection created by Thibaut Goldsborough (a PhD student in Bankhead's group); other developers have integrated it into their own software.
  * **OpenMicroanatomy** and its **QuPath Edu** component, an open-source teaching platform built by medical student Aaron Yli-Hallila (University of Oulu, Finland), used to teach medical students in Finland for years, with pilots in South Africa and Namibia.

### Future

* Two developers currently work on QuPath's code — Bankhead and **Alan O'Callaghan** (research software engineer/postdoc). They have a backlog of ideas for the era of **AI, multiplexed, and multidimensional imaging**, and a year or two of funding to implement them.

### Why open-source

* Bankhead's career was built on open-source software (ImageJ, about which he wrote an open handbook). He saw the lack of a pathology-friendly open platform as "incredibly — even unethically — inefficient": in-house/proprietary tools made it impossible to verify claims, reproduce results, or reuse methods.
* He understands why companies and academics are reluctant to open their code (business case; fear of exposed bugs; the time cost of documentation; the career disincentive to polish rather than publish) — which is precisely why no one had built one, and why he was determined his own software would be open.

### On clinical use (a deliberate "no")

* Bankhead does **not** want QuPath or a derivative approved for clinical use. Adapting it for the clinic would make it less flexible for research and introduce legal/regulatory burdens better handled by companies. He argues the work helps patients and pathologists more effectively by keeping its research priorities.

> "If a problem already gets a lot of attention, then I'd rather spend my time on something else."

### On impact and AI caution

* He believes QuPath has helped labs make substantial cost/efficiency savings — making some studies possible and others unnecessary — and hopes it improved research culture around openness and reproducibility. Because everything is open, "it makes little sense for anyone to publish something new and worse"; open software raises the baseline rather than competing for users.
* On AI-augmented image analysis, he urges caution: the important questions are about *how* the technology is used and *who* benefits (he welcomes studies of automation bias), and there's a "huge difference between a proof-of-concept published in a journal and a genuinely useful software tool."

> "If you're a pathologist, I hope you won't trust what computationally minded people like me claim our tools can do. Rather, I hope you'll try them out where you can, engage critically, ask awkward questions, and help shape how the field continues to develop."

### Source

* [Solving Unpopular Problems: The QuPath Story](https://thepathologist.com/issues/2026/articles/july/solving-unpopular-problems-the-qupath-story/) — Helen Bristow, *The Pathologist*, 29 July 2026 (Interview, \~8 min read).