Evidence map›Paper›PMID 33246195›Full record

ArticleComputers in biology and medicine2021

Effect of deep transfer and multi-task learning on sperm abnormality detection.

Amir Abbasi, Erfan Miahi, Seyed Abolghasem Mirroshandel

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Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.

0numbers the graph read from it
0cells of the map it votes in
21citing papers in PubMed
3.6field-weighted citation impact, top 7% of its field
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

21 citing papers in PubMed, 45 citations in OpenAlex.

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  4. An efficient advanced YOLOv8 framework for sperm motility detection.Journal of assisted reproduction and genetics · 2025
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  5. Standardisation of Dog Sperm Morphology Classification.Reproduction in domestic animals = Zuchthygiene · 2025
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Amir AbbasiDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran. Electronic address: amir.abbasi.rose@gmail.com.
Erfan MiahiDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran. Electronic address: mhi.erfan1@gmail.com.
Seyed Abolghasem MirroshandelDepartment of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran. Electronic address: mirroshandel@guilan.ac.ir.
University of Guilan · IR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Analyzing the abnormality of morphological characteristics of male human sperm has been studied for a long time mainly because it has many implications on the male infertility problem, which accounts for approximately half of the infertility problems in the world. Yet, detecting such abnormalities by embryologists has several downsides. To clarify, analyzing sperms through visual inspection of an expert embryologist is a highly subjective and biased process. Furthermore, it takes much time for a specialist to make a diagnosis. Hence, in this paper, we proposed two deep learning algorithms that are able to automate this process. The first algorithm uses a network-based deep transfer learning approach, while the second technique, named Deep Multi-task Transfer Learning (DMTL), employs a novel combination of network-based deep transfer learning and multi-task learning to classify sperm's head, vacuole, and acrosome as either normal or abnormal. This DMTL technique is capable of classifying all the aforementioned parts of the sperm in a single prediction. Moreover, this is the first time that the concept of multi-task learning has been introduced to the field of Sperm Morphology Analysis (SMA). To benchmark our algorithms, we employed a freely-available SMA dataset named MHSMA. During our experiments, our algorithms reached the state-of-the-art results on the accuracy, precision, and f

Indexed as

Semen AnalysisSpermatozoaAcrosomeAlgorithmsHumansMaleDeep learningHuman sperm morphometryInfertilityMulti-task learningTransfer learning

Identifiers

PMID33246195
OpenAlexW3108155159

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.