Medical Image Perception

Samei, Ehsan; Krupiński, Elizabeth A.

doi:10.1017/9781108163781.001

Cited by 17 publications

(15 citation statements)

References 60 publications

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“…Much progress has been made in understanding the perceptual and cognitive mechanisms that underlie clinical decision-making based on medical images. 10 – 31 Nonetheless, we still do not have a quantitative understanding, especially that of a predictive value, of the underlying processes. For instance, we cannot measure, much less predict, the probability of a given diagnostic outcome given an individual medical image.…”

Section: Introductionmentioning

confidence: 99%

Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study

Hegdé

2020

J. Med. Imag.

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. The scientific, clinical, and pedagogical significance of devising methodologies to train nonprofessional subjects to recognize diagnostic visual patterns in medical images has been broadly recognized. However, systematic approaches to doing so remain poorly established. Using mammography as an exemplar case, we use a series of experiments to demonstrate that deep learning (DL) techniques can, in principle, be used to train naïve subjects to reliably detect certain diagnostic visual patterns of cancer in medical images. In the main experiment, subjects were required to learn to detect statistical visual patterns diagnostic of cancer in mammograms using only the mammograms and feedback provided following the subjects’ response. We found not only that the subjects learned to perform the task at statistically significant levels, but also that their eye movements related to image scrutiny changed in a learning-dependent fashion. Two additional, smaller exploratory experiments suggested that allowing subjects to re-examine the mammogram in light of various items of diagnostic information may help further improve DL of the diagnostic patterns. Finally, a fourth small, exploratory experiment suggested that the image information learned was similar across subjects. Together, these results prove the principle that DL methodologies can be used to train nonprofessional subjects to reliably perform those aspects of medical image perception tasks that depend on visual pattern recognition expertise.

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Section: Introductionmentioning

confidence: 99%

Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study

Hegdé

2020

J. Med. Imag.

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“…Accurate interpretation and classification of medical images is an important component of the diagnosis and treatment of numerous diseases. A wide range of medical disciplines (Samei & Krupinski, 2010 ) ranging from pathology (our focus here), to radiology, to ophthalmology rely on expert analysis of images to detect abnormalities. While the exact rate of diagnostic errors is unknown, consistent evidence suggests that error rates are > 10% (Goldman et al, 1983 ; Hoff, 2013 ; Kirch & Schafii, 1996 ; Shojania, Burton, McDonald, & Goldman, 2003 ; Sonderegger-Iseli, Burger, Muntwyler, & Salomon, 2000 ).…”

Section: Introductionmentioning

confidence: 99%

The impact of speed and bias on the cognitive processes of experts and novices in medical image decision-making

et al. 2018

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Training individuals to make accurate decisions from medical images is a critical component of education in diagnostic pathology. We describe a joint experimental and computational modeling approach to examine the similarities and differences in the cognitive processes of novice participants and experienced participants (pathology residents and pathology faculty) in cancer cell image identification. For this study we collected a bank of hundreds of digital images that were identified by cell type and classified by difficulty by a panel of expert hematopathologists. The key manipulations in our study included examining the speed-accuracy tradeoff as well as the impact of prior expectations on decisions. In addition, our study examined individual differences in decision-making by comparing task performance to domain general visual ability (as measured using the Novel Object Memory Test (NOMT) (Richler et al. Cognition 166:42–55, 2017). Using signal detection theory and the diffusion decision model (DDM), we found many similarities between experts and novices in our task. While experts tended to have better discriminability, the two groups responded similarly to time pressure (i.e., reduced caution under speed instructions in the DDM) and to the introduction of a probabilistic cue (i.e., increased response bias in the DDM). These results have important implications for training in this area as well as using novice participants in research on medical image perception and decision-making.

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“…The same is also true for training, testing and benchmarking machine learning applications designed to perform various analyses of medical images. Similar considerations also apply to clinical and scientific research involving analysis and perception of medical images ( Samei and Elizabeth, 2010 ; Bhattacharyya et al, 2017 ; Marques et al, 2017 ). Altogether, for human experts and expert machines alike, the larger the training/testing medical image set, the better.…”

Section: Introductionmentioning

confidence: 91%

“…For instance, imagine training a radiology resident specializing in mammography. This is the field of medicine in which medical images are easiest to come by, in no small part because a relatively large proportion of eligible women typically undergo mammographic screening for breast cancer annually ( Heath et al, 2001 ; Samei and Elizabeth, 2010 ). However, because the incidence of breast cancer is quite low (0.3–0.5% ( Coldman and Phillips, 2013 ; Njor et al, 2013 ; Welch et al, 2016 )), the proportion of images with cancer accounts for a tiny fraction of the available mammograms.…”

Section: Introductionmentioning

confidence: 99%

Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training

Bart¹,

Hegdé²

2018

Front. Neuroinform.

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Making clinical decisions based on medical images is fundamentally an exercise in statistical decision-making. This is because in this case, the decision-maker must distinguish between image features that are clinically diagnostic (i.e., signal) from a large amount of non-diagnostic features. (i.e., noise). To perform this task, the decision-maker must have learned the underlying statistical distributions of the signal and noise to begin with. The same is true for machine learning algorithms that perform a given diagnostic task. In order to train and test human experts or expert machine systems in any diagnostic or analytical task, it is advisable to use large sets of images, so as to capture the underlying statistical distributions adequately. Large numbers of images are also useful in clinical and scientific research about the underlying diagnostic process, which remains poorly understood. Unfortunately, it is often difficult to obtain medical images of given specific descriptions in sufficiently large numbers. This represents a significant barrier to progress in the arenas of clinical care, education, and research. Here we describe a novel methodology that helps overcome this barrier. This method leverages the burgeoning technologies of deep learning (DL) and deep synthesis (DS) to synthesize medical images de novo. We provide a proof-of-principle of this approach using mammograms as an illustrative case. During the initial, prerequisite DL phase of the study, we trained a publicly available deep learning neural network (DNN), using open-sourced, radiologically vetted mammograms as labeled examples. During the subsequent DS phase of the study, the fully trained DNN was made to synthesize, de novo, images that capture the image statistics of a given input image. The resulting images indicated that our DNN was able to faithfully capture the image statistics of visually diverse sets of mammograms. We also briefly outline rigorous psychophysical testing methods to measure the extent to which synthesized mammography were sufficiently alike their original counterparts to human experts. These tests reveal that mammography experts fail to distinguish synthesized mammograms from their original counterparts at a statistically significant level, suggesting that the synthesized images were sufficiently realistic. Taken together, these results demonstrate that deep synthesis has the potential to be impactful in all fields in which medical images play a key role, most notably in radiology and pathology.

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Medical Image Perception

Cited by 17 publications

References 60 publications

Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study

Deep learning can be used to train naïve, nonprofessional observers to detect diagnostic visual patterns of certain cancers in mammograms: a proof-of-principle study

The impact of speed and bias on the cognitive processes of experts and novices in medical image decision-making

Deep Synthesis of Realistic Medical Images: A Novel Tool in Clinical Research and Training

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