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AI4Life Denoising Challenge 2024: Results

AI4Life Denoising Challenge 2024: Results

by Vera Galinova, Beatriz Serrano-Solano

The AI4Life Denoising Challenge, which aimed to improve the denoising of microscopy images using deep learning, has been successfully completed. With 104 registrations from 27 countries, the challenge gathered participation from the global scientific community, generating 151 total submissions across four distinct leaderboards featuring different datasets containing one of two noise types. 

What was the challenge about?

Microscopy images are indispensable for biological and medical research, but noise introduced during acquisition can impair image quality and complicate interpretation. The challenge’s focus was the unsupervised denoising task, which, unlike supervised learning, does not require pairs of noisy and clean images, simplifying the real-world application of the algorithms. Researchers applied advanced deep learning algorithms to real-world datasets, aiming to improve image quality while preserving essential features such as edges, textures, and fine details. Find all the information on the challenge at https://ai4life.eurobioimaging.eu/denoising-challenge/

Key statistics

  • Total Participants: 104 registrants from 27 countries
  • Total Submissions: 151
  • Leaderboard Submissions:
  • Structured Noise 1: 17 submissions from 7 participants
  • Structured Noise 2: 7 submissions from 5 participants
  • Unstructured Noise 1: 16 submissions from 7 participants
  • Unstructured Noise 2: 13 submissions from 8 participants

Results by category

Structured Noise 1: Hagen et al (50 total submissions)
 

Name

Affiliation

Algorithm

Result

SI-PSNR

Result

SSIM

Code

1

bensalmon

University of Birmingham

COSDD

31.4847

0.5523

Link to code

2

mcroft

Human Technopole

 N2V

30.8962

0.5534

Link to code

3

shanetoy

Seoul National University

 N2V

30.8406

0.5525

Link to code 

Structured Noise 2: SUPPORT (20  total submissions)
 

Name

Affiliation

Algorithm

Result

SI-PSNR

Result

SSIM

Code

1

bensalmon

University of Birmingham

COSDD

30.4415

0.6278

Link to code

2

a897574323

University of Shanghai for Science and Technology

 N2V

29.7744

0.6112

Link to code

3

edoardogiacomello

Human Technopole

 N2V

28.8547

0.5272

Link to code

Unstructured Noise 1: JUMP (47  total submissions)
 

Name

Affiliation

Algorithm

Result

SI-PSNR

Result

SSIM

Code

1

bensalmon

University of Birmingham

COSDD

35.6282

0.9507

Link to code

2

mcroft

Human Technopole

N2V

35.4957 

0.9413

Link to code

3

edoardogiacomello

Human Technopole

N2V

35.4957

0.9412

Link to code

Unstructured Noise 2: W2S (34 total submissions)
 

Name

Affiliation

Algorithm

Result

SI-PSNR

Result

SSIM

Code

1

bensalmon

University of Birmingham

COSDD

35.6855

0.9163

Link to code

2

edoardogiacomello

Human Technopole

N2V2

35.0505 

0.9025

Link to code

3

edoardogiacomello

Human Technopole

N2V

35.0319 

0.9027

Link to code

What’s next?

The AI4Life Denoising Challenge provided a platform for benchmarking denoising methods across various datasets. The challenge showed how unsupervised deep learning algorithms, like COSDD and N2V, can significantly enhance image quality. The leaderboard results asserted the robustness of these methods across different datasets and noise types. 

We encourage researchers to continue to participate in the challenge through late submissions. Please contact us if you want to participate by submitting your algorithm, and the challenge team will assist you. 

Looking ahead, AI4Life plans to host more challenges soon. Stay tuned for upcoming announcements!

 

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AI4Life joins the AI4Europe Community

AI4Life joins the AI4Europe Community

by Beatriz Serrano-Solano

We are excited to announce that AI4Life has officially joined the AI4Europe community, a collaborative platform dedicated to advancing Artificial Intelligence research and innovation across Europe.

The AI-on-Demand (AIoD) platform serves as a community-driven channel designed to ensure quality, trustworthiness, and explainability in AI solutions. Here are some key benefits:

  • Enhanced collaboration: AI4Life members can engage with peers and experts across various disciplines.
  • Access to resources: The AIoD platform provides a wealth of resources, including datasets, tools, services, and educational courses.
  • Dissemination: the platform provides channels to share research outputs, news updates or forthcoming events.

Explore the AI4Life record at https://www.ai4europe.eu/ai-community/projects/ai4life