Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.
Full frame distilled prediction
Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
- Candidate categories
- none
- Consensus categories
- none
- Domain
- Candidate signal: noneConsensus signal: none
- Study design
- Candidate signal: Other designConsensus signal: none
- Genre
- Candidate signal: EmpiricalConsensus signal: Empirical
- Teacher disagreement score
- 0.689
- Threshold uncertainty score
- 0.637
- Validation status
machine_predicted_unvalidated·codex-gemma-dda1882f352a
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.297 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration ap- proaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi- task medical image registration data set for comprehen- sive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, ac- curacy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image regis- tration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state- of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- University of Birmingham Research Portal (University of Birmingham)
- Topic
- Radiomics and Machine Learning in Medical Imaging
- Field
- Medicine
- Canadian institutions
- not available
- Funders
- Menzies Centre for Australian Studies, King's College London, University of LondonComputer Science and Artificial Intelligence Laboratory, Massachusetts Institute of TechnologyStanford Bio-XGillings School of Public HealthHaute école Spécialisée de Suisse OccidentaleNanjing UniversityConcordia UniversityNvidiaRadboud Universitair Medisch CentrumUppsala UniversitetAthinoula A. Martinos Center for Biomedical Imaging, Massachusetts General HospitalCentre National de la Recherche ScientifiqueTel Aviv UniversityImperial College LondonCentre d'Imagerie BioMédicaleUniversität zu LübeckUniversité Paris-SaclayTsinghua UniversityUniversity of North Carolina at Chapel HillNanjing University of Information Science and TechnologyWuhan National Laboratory for OptoelectronicsElektaRadboud UniversiteitChinese University of Hong KongBundesministerium für Bildung und ForschungHuazhong University of Science and TechnologyInstitut Gustave-RoussyVanderbilt UniversityKing's College LondonInstitut National de la Santé et de la Recherche MédicaleAkademia Górniczo-Hutnicza im. Stanislawa StaszicaMassachusetts General Hospital
- Keywords
- Image registrationArtificial intelligenceComputer scienceDeep learningTask (project management)Computer visionMedical imagingImage (mathematics)Engineering
- Has abstract in OpenAlex
- yes