AI researcher at the intersection of brain tumor imaging AI and generative vision modeling
2
papers here
1
reviewed by Evolve
0
first-author
0
corresponding
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works · OpenAlex
0
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-09-21) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
No reliable output data: the record reports h-index and i10-index of 0 with only 2 recent works, suggesting thin author-profile linkage rather than measured productivity. This axis would measure total publications and output trends over time; it is not scored here.
Impact—
Impact cannot be scored: no usable citation data exist in the record (h-index and i10-index are 0, consistent with weak profile linkage). This axis would measure citation counts, citation trajectory, and the scholarly uptake of the work.
Focus100/100
The focus score is 100. The field distribution contains a single field, 'medical imaging ai (neuroradiology)', and all five recorded topics — glioma diagnosis and treatment, radiomics and machine learning in medical imaging, GANs and image synthesis, image enhancement, and visual attention and saliency detection — orbit one theme, with both 2026 preprints continuing the same span.
Collaboration70/100
The collaboration score is 70. The OpenAlex record lists 33 distinct coauthors alongside a recurring core of roughly a dozen frequent collaborators, indicating team-embedded rather than solo work. Set against only 2 works in the last three years, this is a wide network per paper, consistent with the large consortium-style authorship typical of foundation-model studies.
Activity32/100
The activity score is 32. Counts by year show exactly 2 publications, both dated 2026, matching recent3yWorks = 2 and the two arXiv preprints in the catalog. Volume is low and concentrated in a single year, with nothing earlier in this record — an early or thinly documented recent output rather than a sustained multi-year publishing rhythm.
Reproducibility55/100
The reproducibility score is 55, averaged from just 1 assessed paper (reproMean = 55, reproPapers = 1). The reviewed brain tumor vision-language foundation model is rated medium reproducibility readiness; Evolve finds its validation broad and convincing but notes internal inconsistencies, the main risk for reproduction. The FRPSS paper has not been assessed.
Research summary
Bing Wang is an AI researcher whose documented work sits at the intersection of medical imaging AI and generative vision; the profile is name-resolved from OpenAlex bibliographic data, with no institution, country, or ORCID attached. The record shows two 2026 arXiv preprints. The first, a middle-author contribution to a vision-language foundation model for brain tumor diagnosis from preoperative multimodal data, has been reviewed by Evolve: the model reportedly matches radiologists in diagnosing 12 types of brain tumors from MRI with calibrated uncertainty, and its validation is broad and convincing, though internal inconsistencies are noted; reproducibility readiness is medium. The second, FRPSS, a last-author paper, proposes feature rearrangement in pre-shape space for single-image generation. Recorded topics — glioma diagnosis and treatment, radiomics and machine learning in medical imaging, generative adversarial networks and image synthesis, image enhancement, and visual attention and saliency detection — form one coherent cluster around brain imaging and vision methods, yielding a maximal focus score of 100. Collaboration is broad (33 distinct coauthors plus a recurring core team), while recent activity is modest at 2 works, both 2026, and citation-based impact cannot yet be assessed.
Collaboration pattern
The record lists 33 distinct coauthors and a recurring core of about a dozen frequent collaborators, including Yinong Wang, Jianwen Chen, Zhou Chen, Shuwen Kuang, Haoning Jiang, Lei Wu, and Bin Tang. Relative to only 2 recent works, that is a wide network per paper, consistent with consortium-style foundation-model authorship: a middle-author slot on the reviewed brain tumor paper and a last-author slot on FRPSS.
Research keywords
Highlights
Vision-language foundation model for 12-type brain tumor diagnosis
Calibrated uncertainty and WHO CNS5 framing target clinical use
FRPSS: last-author work on single-image generation
A 2026 arXiv preprint with Bing Wang in the last author position, FRPSS rearranges features in pre-shape space for single-image generation. It sits outside the reviewed set: no Evolve verdict, keywords, or reproducibility rating yet.
Timeline
- 2026arXiv preprint: a vision-language foundation model for brain tumor diagnosis from preoperative multimodal data (middle author; Evolve-reviewed, medium reproducibility readiness).
- 2026arXiv preprint: FRPSS — feature rearrangement in pre-shape space for single-image generation (last author).
- 2026OpenAlex records both works under 2026 (countsByYear 2026: 2; recent3yWorks = 2), with 33 distinct coauthors listed.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- Bibliometrics come from OpenAlex only; no institution, country, or ORCID is available, and 'Bing Wang' is a common name, so this profile is name-resolved from public bibliographic data.
- Citation-based metrics (h-index, i10-index, citation counts) are effectively absent from the record, so the output and impact dimensions remain unscored.
- Only two 2026 arXiv preprints are in the catalog; the FRPSS paper carries no Evolve verdict, keywords, or reproducibility assessment.
- Co-author names in the source data include disambiguation variants such as '(Joyce)', so the 33-coauthor figure may contain duplicates or noise.