Evidence map›Paper›PMID 39821779›Full record

ArticleJournal of imaging informatics in medicine2025

HCTTI: High-Performance Heterogeneous Computing Toolkit for Tissue Image Stain Normalization.

Yan Jiang, Bo Wang, Weipeng Xing, Cheng Huo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of imaging informatics in medicine, 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

4 authors.

Yan JiangSchool of Software, Quanzhou University of Information Engineering, Quanzhou, Fujian, 362000, China. jianghnu@hnu.edu.cn.ORCID http://orcid.org/0000-0002-4228-5441
Bo WangScience Department, Geneis (Beijing) Co. Ltd., Beijing, 100102, P. R. China.
Weipeng XingSchool of Electrical & Information Engineering, Anhui University of Technology, Maanshan, Anhui, 243002, China.
Cheng HuoDepartment of Pathology, Sinopharm Tongmei General Hospital, Datong, 037003, Shanxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Whole slide imaging (WSI) has transformed diagnostic medicine, particularly in the field of cancer diagnosis and treatment. The use of deep learning algorithms for predicting WSIs has opened up new avenues for advanced medical diagnostics. Additionally, stain normalization can reduce the color and intensity variations present in WSI from different hospitals. As a result, deep learning classification accuracy improves. However, WSI reading and color normalization are still largely performed by using CPUs, leading to sub-optimal performance. We proposed a High-Performance Heterogeneous Computing Toolkit for Tissue Image (HCTTI) that integrates multiple computer system-level optimizations and encompasses WSI reading, tile normalization, and tile saving. We explored the potential advantages and limitations of different WSI readers and color normalization techniques in WSI analysis and the performance of different tile serialization formats for saving tiles. We found that HCTTI is 7

Indexed as

Image Interpretation, Computer-AssistedImage Processing, Computer-AssistedNeoplasmsAlgorithmsDeep LearningHumansDigital pathologyDistributed computingHistopathology image analysisOpen source softwareStain normalization

Identifiers

PMID39821779
PMCPMC12572583

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.