Evidence map›Paper›PMID 36101363›Full record

ArticleBiology2022

Divide-and-Attention Network for HE-Stained Pathological Image Classification.

Rui Yan, Zhidong Yang, Jintao Li, Chunhou Zheng, Fa Zhang

Abstract read
In one paragraph

Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Review
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

5 authors.

Rui YanHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China.ORCID 0000-0002-1336-1740
Zhidong YangHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China.
Jintao LiHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China.
Chunhou ZhengSchool of Artificial Intelligence, Anhui University, Hefei 230093, China.
Fa ZhangHigh Performance Computer Research Center, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100045, China.ORCID 0000-0002-2081-9369

Funding

the National Key Research and Development Program of China No.2021YFF0704300the NSFC projects grants 61932018, 62072441 and 62072280the Strategic Priority Research Program of the Chinese Academy of Sciences No. XDA16021400
6 · The paper itself

Abstract

Since pathological images have some distinct characteristics that are different from natural images, the direct application of a general convolutional neural network cannot achieve good classification performance, especially for fine-grained classification problems (such as pathological image grading). Inspired by the clinical experience that decomposing a pathological image into different components is beneficial for diagnosis, in this paper, we propose a

Indexed as

attention mechanismconvolutional neural networkknowledge embeddingpathological image classification

Identifiers

PMID36101363
PMCPMC9311575

What OpenQuestion holds

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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.