Channel Embedding for Informative Protein Identification from Highly Multiplexed Images

Magid, Salma Abdel; Jang, Won-Dong; Schapiro, Denis; Wei, Donglai; Tompkin, James; Sorger, Peter K.; Pfister, Hanspeter

doi:10.1101/2020.03.24.004085

Cited by 4 publications

(7 citation statements)

References 19 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Regarding the technical approach to provide transparency, the incorporation of medical experts motivated designers to incorporate prior knowledge directly into the model structure and/or inference for medical imaging (73%/64% articles with/without the incorporation of end users do not need a second model to generate transparency). enabled the generation of pixel-attribution methods 54 to visualize pixel-level importance for a specific class of interest [55][56][57][58][59][60][61][62][63][64][65][66][67][68][69] . In segmentation tasks, where clinically relevant abnormalities and organs are usually of small sizes, features from different resolution levels were aggregated to compute attention and generate more accurate outcomes, as demonstrated in multiple applications, e.g., multi-class segmentation in fetal Magnetic Resonance Imagings (MRIs) 58 and multiple sclerosis segmentation in MRIs 61 .…”

Section: In: Incorporationmentioning

confidence: 99%

“…64 a deletion curve was constructed by plotting the dice score vs. the percentage of pixels removed and ref. 55 defined a recall rate when the model proposes certain number of informative channels 95 proposed to evaluate the consistency of visualization results and the outputs of a CNN by computing the L1 error between predicted class scores and explanation pixel-attribution maps. In summary, while the methods grouped in this theme are capable of evaluating how well a method aligns with it's intended mechanism of transparency, they fall short of capturing any human factors-related aspects of transparency design.…”

Section: R: Reportingmentioning

confidence: 99%

See 1 more Smart Citation

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

Section: In: Incorporationmentioning

confidence: 99%

Section: R: Reportingmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

show abstract

“…In order to quantify how these features may be important, we use a deep learning method that quantifies the channel-wise importance for reconstructing imaging features across all channels. A similar method to the one described here uses the gradient of the model to determine the channel-wise importance for cell type classification [28]. The key difference of our proposed method is the objective of the model (reconstruction instead of classification) which requires a different architecture.…”

Section: Deep Learning Gradient-based Selectionmentioning

confidence: 99%

Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays

Ternes

Lin²,

Chen³

et al. 2022

PLoS Comput Biol

View full text Add to dashboard Cite

Recent state-of-the-art multiplex imaging techniques have expanded the depth of information that can be captured within a single tissue sample by allowing for panels with dozens of markers. Despite this increase in capacity, space on the panel is still limited due to technical artifacts, tissue loss, and long imaging acquisition time. As such, selecting which markers to include on a panel is important, since removing important markers will result in a loss of biologically relevant information, but identifying redundant markers will provide a room for other markers. To address this, we propose computational approaches to determine the amount of shared information between markers and select an optimally reduced panel that captures maximum amount of information with the fewest markers. Here we examine several panel selection approaches and evaluate them based on their ability to reconstruct the full panel images and information within breast cancer tissue microarray datasets using cyclic immunofluorescence as a proof of concept. We show that all methods perform adequately and can re-capture cell types using only 18 of 25 markers (72% of the original panel size). The correlation-based selection methods achieved the best single-cell marker mean intensity predictions with a Spearman correlation of 0.90 with the reduced panel. Using the proposed methods shown here, it is possible for researchers to design more efficient multiplex imaging panels that maximize the amount of information retained with the limited number of markers with respect to certain evaluation metrics and architecture biases.

show abstract

“…A measure related to completeness was defined in [43] and aimed to capture the proportion of training images represented by the learned visual concepts, in addition to two other metrics: the inter-and intraclass diversity and the faithfulness of explanations computed by perturbing relevant patches and measuring the drop in classification confidence. Other articles followed a similar approach to validate relevant pixels or features identified with a transparent method; for example, in [83] a deletion curve was constructed by plotting the dice score vs. the percentage of pixels removed and [1] defined a recall rate when the model proposes certain number of informative channels. [111] proposed to evaluate the consistency of visualization results and the outputs of a CNN by computing the L1 error between predicted class scores and explanation heatmaps.…”

Section: R: Reportingmentioning

confidence: 99%

“…The complex nature of both 3D imaging in radiology and pathological images makes image analysis tasks more time consuming than 2D image analysis that is more prevalent in other specialities, such as dermatology, which motivates transparency as an alternative to complete human image analysis to save time while retaining trustworthiness. In detail, classification problems in 3D radiological images and pathological images included abnormality detection in computed tomography (CT) scans [3,5,61,47,89,107,111,112], MRIs [34,11,38,51,59,87,85,50,95,77,78,98,100,104], pathology images [1,24,26,27,30,34,37,40,82,84,50,74,76,108,5] and positron emission tomography (PET) images [68]. Mammography dominated the 2D radiology image applications [60,88,44,45,86,96,99,…”

Section: T: Taskmentioning

confidence: 99%

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

Chen¹,

Gómez²,

Huang³

et al. 2021

Preprint

View full text Add to dashboard Cite

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.

show abstract

Channel Embedding for Informative Protein Identification from Highly Multiplexed Images

Cited by 4 publications

References 19 publications

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

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

Computational multiplex panel reduction to maximize information retention in breast cancer tissue microarrays

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

Contact Info

Product

Resources

About