Evidence map›Paper›PMID 41684746›Full record

ArticleProceedings of machine learning research2025

Raptor: Scalable Train-Free Embeddings for 3D Medical Volumes Leveraging Pretrained 2D Foundation Models.

Ulzee An, Moonseong Jeong, Simon A Lee, Aditya Gorla, Yuzhe Yang, Sriram Sankararaman

Abstract read

In one paragraph

Article in Proceedings of machine learning research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Ulzee AnComputer Science Department, University of California, Los Angeles.

Moonseong JeongComputer Science Department, University of California, Los Angeles.

Simon A LeeDepartment of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles.

Aditya GorlaDepartment of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles.

Yuzhe YangComputer Science Department, University of California, Los Angeles.

Sriram SankararamanComputer Science Department, University of California, Los Angeles.

Funding

Combining Voice and Genetic Information to Detect Heterogeneity in Major Depressive DisorderR01MH122569 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI FLINT, JONATHAN · 2020 to 2024

$3.4M

Improving the interpretability of genetic studies of major depressive disorder to identify risk genesR01MH130581 · NIMH · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI JONATHAN FLINT, KENNETH SEEDMAN KENDLER · 2022 to 2026

$2.8M

Expressive and scalable statistical models for genomic and biomedical dataR35GM153406 · NIGMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI Sriram Sankararaman · 2024 to 2026

$1.0M

NIGMS NIH HHS R35 GM153406NIMH NIH HHS R01 MH122569NIMH NIH HHS R01 MH130581

6 · The paper itself

Abstract

Current challenges in developing foundational models for volumetric imaging data, such as magnetic resonance imaging (MRI), stem from the computational complexity of training state-of-the-art architectures in high dimensions and curating sufficiently large datasets of volumes. To address these challenges, we introduce

Identifiers

PMID41684746

PMCPMC12893380

What OpenQuestion holds

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LicenceTDM

Read underepoch 390

Registered trials

None linked

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.