Evidence map›Paper›PMID 42633391›Full record

ArticleKDD : proceedings. International Conference on Knowledge Discovery & Data Mining2025

Saliency-Bench: A Comprehensive Benchmark for Evaluating Visual Explanations.

Yifei Zhang, James Song, Siyi Gu, Tianxu Jiang, Bo Pan, Guangji Bai, Liang Zhao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. ExplainTS: a benchmark dataset of pretrained models andFrontiers in artificial intelligence · 2026
    Article
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

7 authors.

Yifei ZhangEmory University, Atlanta, GA, USA.
James SongEmory University, Atlanta, GA, USA.
Siyi GuStanford University, Stanford, CA, USA.
Tianxu JiangUniversity of Michigan-Ann Arbor, Ann Arbor, MI, USA.
Bo PanEmory University, Atlanta, GA, USA.
Guangji BaiEmory University, Atlanta, GA, USA.
Liang ZhaoEmory University, Atlanta, GA, USA.

Funding

Explainable and Ethical AI for Studying Alzheimer's DiseaseR01AG089806 · NIA · EMORY UNIVERSITY · PI JAMES J LAH, Deqiang Qiu · 2024 to 2026
$2.1M
SCH: Visual explanation-guided learning for human-AI collaborative abdominal cancer diagnostic imagingR01CA297856 · NCI · EMORY UNIVERSITY · PI Liang Zhao · 2024 to 2026
$872k
NCI NIH HHS R01 CA297856NIA NIH HHS R01 AG089806
6 · The paper itself

Abstract

Explainable AI (XAI) has gained significant attention for providing insights into the decision-making processes of deep learning models, particularly for image classification tasks through saliency-based visual explanations. Despite their success, key challenges persist due to the scarcity of annotated datasets and the absence of standardized evaluation protocols. In this paper, we introduce

Indexed as

Computer VisionExplainable AISaliency MethodsVisual Explanation

Identifiers

PMID42633391
PMCPMC13499442

What OpenQuestion holds

Textmetadata
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.