Intelligent medical image grouping through interactive learning

Guo, Xuan; Yu, Qi; Li, Rui; Alm, Cecilia Ovesdotter; Calvelli, Cara; Shi, Peng; Haake, Anne R.

doi:10.1007/s41060-016-0021-2

Cited by 6 publications

(9 citation statements)

References 30 publications

(33 reference statements)

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“…Relevant works have proved the effectiveness of deep interactive learning. Xuan 18,19 proved that combined with interactive learning. The model is aligned with histologists' sense-making.…”

Section: Deep Interactive Learning For Multiple Cell Types Segmentationmentioning

confidence: 99%

Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules

Liang

Zang

et al. 2022

Cytometry Pt A

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The development of mouse spermatozoa is a continuous process from spermatogonia, spermatocytes, spermatids to mature sperm. Those developing germ cells (spermatogonia, spermatocyte, and spermatids) together with supporting sertoli cells are all enclosed inside seminiferous tubules of the testis, their identification is key to testis histology and pathology analysis. Automated segmentation of all these cells is a challenging task because of their dynamical changes in different stages. The accurate segmentation of testicular cells is critical in developing computerized spermatogenesis staging. In this paper, we present a novel segmentation model, SED‐Net, which incorporates a squeeze‐and‐excitation (SE) module and a dense unit. The SE module optimizes and obtains features from different channels, whereas the dense unit uses fewer parameters to enhance the use of features. A human‐in‐the‐loop strategy, named deep interactive learning, is developed to achieve better segmentation performance while reducing the workload of manual annotation and time consumption. Across a cohort of 274 seminiferous tubules from stages VI to VIII, the SED‐Net achieved a pixel accuracy of 0.930, a mean pixel accuracy of 0.866, a mean intersection over union of 0.710, and a frequency weighted intersection over union of 0.878, respectively, in terms of four types of testicular cell segmentation. There is no significant difference between manual annotated tubules and segmentation results by SED‐Net in cell composition analysis for tubules from stages VI to VIII. In addition, we performed cell composition analysis on 2346 segmented seminiferous tubule images from 12 segmented testicular section results. The results provided quantitation of cells of various testicular cell types across 12 stages. The rule reflects the cell variation tendency across 12 stages during development of mouse spermatozoa. The method could enable us to not only analyze cell morphology and staging during the development of mouse spermatozoa but also potentially could be applied to the study of reproductive diseases such as infertility.

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“…Relevant works have proved the effectiveness of deep interactive learning. Xuan 18,19 proved that combined with interactive learning. The model is aligned with histologists' sense-making.…”

Section: Deep Interactive Learning For Multiple Cell Types Segmentationmentioning

confidence: 99%

Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules

Liang

Zang

et al. 2022

Cytometry Pt A

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“…Various feature-based transparency method were proposed for transparent learning. [36,42,84] first encoded images to deep features and then clustered samples based on these deep features for prediction or image grouping tasks. Feature importance was also well-studied to identify features that are most relevant for a specific class by feature perturbation [98,112] and gradients [76].…”

Section: The Use Of An Attention Mechanism Was the Most Commonmentioning

confidence: 99%

“…However, neither the team (expert + AI) nor expert baseline performance was evaluated. The benefit of involving a dermatologist to complete an image grouping task was demonstrated in [36], in which domain knowledge was used to constrain updates of the algorithm's training, resulting in a better grouping performance than a fully automated method. The user evaluation only measured the task performance.…”

Section: R: Reportingmentioning

confidence: 99%

“…[27] leveraged the transparency from the correlation between the changes of polarization characteristics and the pathological development of cervical precancerous lesions. Clinical knowledge from human experts was used to refine an image grouping algorithm through an interactive mechanism in which experts iteratively provided inputs to the model [36].…”

Section: Pr: Priorsmentioning

confidence: 99%

“…Other segmentation problems included mass segmentation in mammograms [86], cardiac segmentation in ultrasound [73], liver tumor segmentation in hepatic CT images, and skin lesion segmentation in dermatological images [34]. There also existed other applications, e. g., image grouping in dermatological images [36] and image enhancement (super resolution task) in brain MRIs [93] and cardiac MRIs [73].…”

Section: T: Taskmentioning

confidence: 99%

See 2 more Smart Citations

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

Chen¹,

Gómez²,

Huang³

et al. 2021

Preprint

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Transparency in Machine Learning (ML), including interpretable or explainable ML, attempts to reveal the working mechanisms of complex models, including deep neural networks. Transparent ML promises to advance human factors engineering goals of human-centered AI, such as increasing trust or reducing automation bias, in the target users. However, a core requirement in achieving these aims is that the method is indeed transparent to the users. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i. e., a relationship between algorithm and user; as a result, iterative prototyping and evaluation with users is critical to attaining adequate solutions that afford transparency. However, following human-centered design principles in highly specialized and high stakes domains, such as healthcare and medical image analysis, is challenging due to the limited availability of and access to end users, such as domain experts, providers, or patients. This dilemma is further exacerbated by the high knowledge imbalance between ML designers and those end users, which implies an even greater need for iterated development.To investigate the state of transparent ML in medical image analysis, we conducted and now present a systematic review of the literature. Our review reveals multiple severe shortcomings in the design and validation of transparent ML for medical image analysis applications. We find that most studies to date approach transparency as a property of the model itself, similar to task performance, without considering end users during neither development nor evaluation. Despite the considerable difference between the roles and knowledge of ML developers and clinical stakeholders, no study reported formative user research to inform the design and development of transparent ML models; moreover, only a few studies validated transparency claims through empirical user evaluations. The oversight of considering ML transparency as a relationship with end users, the lack of user research, and the sporadic validation of transparency claims put contemporary research on transparent ML for medical image analysis at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research while acknowledging the challenges of human-centered design in healthcare, we introduce the INTRPRT guideline, a systematic design directive for transparent ML systems in medical image analysis. To bridge the disconnect between ML system designers and end users and avoid misspending costly development efforts on models that are finally validated as non-transparent to end users, the INTRPRT guideline suggests formative user research as the first step of transparent model design to understand user needs and domain requirements. Following this process produces evidence to support * Joint first authors.Preprint. Under review.

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Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

et al. 2022

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Transparency in Machine Learning (ML), often also referred to as interpretability or explainability, attempts to reveal the working mechanisms of complex models. From a human-centered design perspective, transparency is not a property of the ML model but an affordance, i.e., a relationship between algorithm and users. Thus, prototyping and user evaluations are critical to attaining solutions that afford transparency. Following human-centered design principles in highly specialized and high stakes domains, such as medical image analysis, is challenging due to the limited access to end users and the knowledge imbalance between those users and ML designers. To investigate the state of transparent ML in medical image analysis, we conducted a systematic review of the literature from 2012 to 2021 in PubMed, EMBASE, and Compendex databases. We identified 2508 records and 68 articles met the inclusion criteria. Current techniques in transparent ML are dominated by computational feasibility and barely consider end users, e.g. clinical stakeholders. Despite the different roles and knowledge of ML developers and end users, no study reported formative user research to inform the design and development of transparent ML models. Only a few studies validated transparency claims through empirical user evaluations. These shortcomings put contemporary research on transparent ML at risk of being incomprehensible to users, and thus, clinically irrelevant. To alleviate these shortcomings in forthcoming research, we introduce the INTRPRT guideline, a design directive for transparent ML systems in medical image analysis. The INTRPRT guideline suggests human-centered design principles, recommending formative user research as the first step to understand user needs and domain requirements. Following these guidelines increases the likelihood that the algorithms afford transparency and enable stakeholders to capitalize on the benefits of transparent ML.

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Intelligent medical image grouping through interactive learning

Cited by 6 publications

References 30 publications

Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules

Deep SED‐Net with interactive learning for multiple testicular cell types segmentation and cell composition analysis in mouse seminiferous tubules

INTRPRT: A Systematic Review of and Guidelines for Designing and Validating Transparent AI in Medical Image Analysis

Explainable medical imaging AI needs human-centered design: guidelines and evidence from a systematic review

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