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Original-Research: Aiforia Technologies Oyj - from NuWays AG
11.02.2026 / 09:00 CET/CEST
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Classification of NuWays AG to Aiforia Technologies Oyj
Company Name: Aiforia Technologies Oyj
ISIN: FI4000507934
Reason for the research: Update
Recommendation: BUY
Target price: EUR 3.8
Target price on sight of: 12 months
Last rating change:
Analyst: Julius Neittamo
Aiforia's tech advances to expedite path to profitability
Aiforia will publish H2'25 results on March 6. We view FY26 as an inflection
year, with stronger revenue growth and operational efficiencies accelerating
the path to profitability.
Aiforia recently launched its proprietary Foundation Engine, a
patent-pending technology based on the Vision Transformer (ViT)
architecture. The ViT architecture is seen to adapt well to the
heterogeneous, challenging features of whole slide images. It is a
relatively novel architecture, challenging Convolutional Neural Networks
(CNN) long-standing dominance in medical imaging. Through global
self-attention, ViTs capture the broader context of an image more
effectively than local convolutions. Takahashi et al. (2024) reviewed 36
medical imaging studies (X-rays, CT scans, MRIs, histopathology slides),
comparing ViTs/Transformers to CNNs, finding ViTs performed generally better
or comparably in many classification tasks (e.g. skin cancer, ViT 94.3%
accuracy).
Aiforia's Foundation Engine exemplifies this ViT advantage, but it also
highlights the fundamental trade-offs between Foundation Models (FM) and
traditional CNNs in pathology workflows. Pre-trained on vast unlabeled
datasets, FMs deploy and scale rapidly while generalising well across a vast
spectrum of cases, but trade cell-level granularity for broader slide
context at the cost of slower, compute-heavy inference. CNNs on the other
hand, deliver precise cell-level detail more efficiently, yet struggle in
novel cases outside their training data, requiring vast annotated datasets,
which pathology desperately lacks. Therefore, training AI models with CNNs
can prove difficult, costly and time-consuming.
Aiforia sees it as essential to bridge both worlds for clinical use,
combining Foundation Models' broad generalisation with task-specific
fine-tuning for validated, precise performance. Foundation Engine-trained
models should generalise well across different cancers and scanners, while
providing cell-level precision with leaner, faster inference than pure FMs.
By leveraging pre-trained FMs, the Foundation Engine gains a 'headstart'
that requires fewer annotated slides for training, which accelerates
development and slashes costs. We expect Foundation Engine models to exhibit
improved accuracy in edge cases (vs CNN), where even experienced
pathologists struggle to reach consensus. Overall, Foundation Engines will
drive faster deployment, superior adaptability, lower cost, and enhanced
clinical scalability. Noteworthy, the latest CE-IVD marked gastric cancer
solution was built using the Foundation Engine.
We remain upbeat on Aiforia's Foundation Engine and its clinical and
commercial scalability. Aiforia's clinical footprint now covers breast,
prostate, and lung cancers, strengthened by its new gastric cancer model.
Supply-demand dynamics are supportive as hospitals face rising pressure to
digitise pathology and adopt AI. In Lombardia, ~400 pathologists in 2025 are
expected to fall by ~100 (due to retirements) by 2027 with only nine
replacements, underscoring urgent automation needs. FY26 should bring
additional tenders, including HUS, Finland's largest hospital group
(potentially in Q2). We reiterate our BUY rating with a EUR 3.80 PT based on
our DCF model.
You can download the research here:
https://eqs-cockpit.com/c/fncls.ssp?u=0eb1762657d6efcf57b1b890081baf53
For additional information visit our website:
https://www.nuways-ag.com/research-feed
Contact for questions:
NuWays AG - Equity Research
Web: www.nuways-ag.com
Email: research@nuways-ag.com
LinkedIn: https://www.linkedin.com/company/nuwaysag
Adresse: Mittelweg 16-17, 20148 Hamburg, Germany
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