ArticleDomain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin...2022
Discriminative, Restorative, and Adversarial Learning: Stepwise Incremental Pretraining.
Article in Domain adaptation and representation transfer : 4th MICCAI Workshop, DART 2022, held in conjunction with MICCAI 2022, Singapore, September 22, 2022, proceedings. Domain Adaptation and Representation Transfer (Workshop) (4th : 2022 : Sin..., 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
5 citing papers in PubMed.
- Autodidactic dense anatomical models.Medical image analysis · 2026Article
- Representing Part-Whole Hierarchies in Foundation Models by Learning Localizability, Composability, and Decomposability from Anatomy via Self-Supervision.Proceedings. IEEE Computer Society Conference on Computer Vision and Pattern Recognition · 2024Article
- Self-supervised learning for medical image analysis: Discriminative, restorative, or adversarial?Medical image analysis · 2024Article
- A survey of the impact of self-supervised pretraining for diagnostic tasks in medical X-ray, CT, MRI, and ultrasound.BMC medical imaging · 2024Review
- Seeking an optimal approach for Computer-aided Diagnosis of Pulmonary Embolism.Medical image analysis · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Uniting three self-supervised learning (SSL) ingredients (discriminative, restorative, and adversarial learning) enables collaborative representation learning and yields three transferable components: a discriminative encoder, a restorative decoder, and an adversary encoder. To leverage this advantage, we have redesigned five prominent SSL methods, including Rotation, Jigsaw, Rubik's Cube, Deep Clustering, and TransVW, and formulated each in a
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.