ArticleQuantitative imaging in medicine and surgery2022
Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Neuroimaging Modalities Used for Ischemic Stroke Diagnosis and Monitoring.Medicina (Kaunas, Lithuania) · 2023Review
- Radiomics and artificial neural networks modelling for identification of high-risk carotid plaques.Frontiers in cardiovascular medicine · 2023Article
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Authors and funding
5 authors.
Funding
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Abstract
backgroundGradient-recalled echo (GRE) sequence is time-consuming and not routinely performed. Herein, we aimed to investigate the ability of weakly supervised learning to identify acute ischemic stroke (AIS) and concurrent hemorrhagic infarction based on diffusion-weighted imaging (DWI).
methodsFirst, we proposed spatially locating small stroke lesions in different positions and hemorrhagic infarction lesions by residual neural and visual geometry group networks using weakly supervised learning. Next, we compared the sensitivity and specificity for identifying automatically concurrent hemorrhagic infarction in stroke patients with the sensitivity and specificity of human readings of diffusion and b
resultsData from a total of 1,027 patients were analyzed. The residual neural network displayed a higher sensitivity than did the visual geometry group network in spatially locating the small stroke and hemorrhagic infarction lesions. The residual neural network had significantly greater patient-level sensitivity than did the human readers (98.4% versus 86.2%, P=0.008) in identifying concurrent hemorrhagic infarction with GRE as the reference standard; however, their specificities were comparable (95.4% versus 96.9%, P>0.99). Weak labeling of lesions required significantly less time than did full labeling of lesions (2.667 versus 10.115 minutes, P<0.001).
conclusionsWeakly supervised learning was able to spatially locate small stroke lesions in different positions and showed more sensitivity than did human reading in identifying concurrent hemorrhagic infarction based on DWI. The proposed approach can reduce the labeling workload.
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