Title: The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants
In recent work, the Vanderbilt Holistic Processing Tests for novel objects (VHPT-NOs), were used to show that holistic processing for artificial objects increased as a function of parametric variation of experience. Here, novices are tested on the VHPT-Nos to address two questions. First, does the test detect any level of holistic processing for novel objects in novices? Second, how is part matching performance on this test related to object recognition ability, as measured by the Novel Object Memory Test (NOMT)? In a high-powered study, we provide substantial evidence of no holistic processing on the VHPT-NO in novices, including for arguably facelike symmetrical Greebles. Evidence of no correlations between measures of holistic processing suggests that these indices can be considered free of influences from domain-general selective attention. In contrast, overall performance in part matching in the VHPT-NO shows shared variance across categories, which we postulate is related to object
recognition. A second study provides direct evidence that part matching measures to a large extent the same ability as whole object learning on the NOMT. Our results suggest that any holistic processing measured in the VHPT-NOs will not be contaminated by domain-general effects and can be considered entirely due to experience with a category. The VHPT-NO will therefore be useful in further examination of how different aspects of experience contribute to the development of holistic processing. more »« less
Chua, K.-W.; Gauthier, I.(
, Journal of experimental psychology)
null
(Ed.)
Holistic processing refers to the processing of objects as wholes rather than in a piecemeal, part-based fashion. Despite a suggested link between expertise and holistic processing, the role of experience in determining holistic processing of both faces and objects has been questioned. Here, we combine an individual differences approach with an experimental training study and parametrically manipulate experience with novel objects to examine the determinants of holistic processing. We also measure object-recognition ability. Our results show that although domain-general visual ability is a predictor of the ability to match object parts, it is the amount of experience people have individuating objects of a category that determines the extent to which they process new objects of this category in a holistic manner. This work highlights the benefits of dissociating the influences of domain-general ability and domain-specific experience, typically confounded in measures of performance or “expertise.” Our findings are consistent with those in recent work with faces showing that variability specific to experience is a better predictor of domain-specific effects than is variability in performance. We argue that individual differences in holistic processing arise from domain-specific experience and that these effects are related to similar effects of experience on other measures of selective attention.
People can relatively easily report summary properties for ensembles of objects, suggesting that this information can enrich visual experience and increase the efficiency of perceptual processing. Here, we ask whether the ability to judge diversity within object arrays improves with experience. We surmised that ensemble judgments would be more accurate for commonly experienced objects, and perhaps even more for objects of expertise like faces. We also expected improvements in ensemble processing with practice with a novel category, and perhaps even more with repeated experience with specific exemplars. We compared the
effect of experience on diversity judgments for arrays of objects, with participants being tested with either a small number of repeated exemplars or with a large number of exemplars from the same object category. To explore the role of more prolonged experience, we tested participants with completely novel objects (random-blobs), with objects familiar at the category level (cars), and with objects with which observers are experts at subordinate-level recognition (faces). For objects that are novel, participants showed evidence of improved ability to distribute attention. In contrast, for object categories with long-term experience, i.e., faces and cars, performance improved during the experiment but not necessarily due to improved ensemble processing. Practice with specific exemplars did not result in better diversity judgments for all object categories. Considered together, these results suggest that ensemble processing improves with experience. However, the role of experience is rapid, does not rely on exemplar-level knowledge and may not benefit from subordinate-level expertise.
Wevodau, Z.; Doshna, B.; Jhala, N.; Akhtar, I.; Obeid, I.; Picone, J.(
, Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB))
Obeid, I.
(Ed.)
The Neural Engineering Data Consortium (NEDC) is developing the Temple University Digital Pathology Corpus (TUDP), an open source database of high-resolution images from scanned pathology samples [1], as part of its National Science Foundation-funded Major Research Instrumentation grant titled “MRI: High Performance Digital Pathology Using Big Data and Machine Learning” [2]. The long-term goal of this project is to release one million images. We have currently scanned over 100,000 images and are in the process of annotating breast tissue data for our first official corpus release, v1.0.0. This release contains 3,505 annotated images of breast tissue including 74 patients with cancerous diagnoses (out of a total of 296 patients). In this poster, we will present an analysis of this corpus and discuss the challenges we have faced in efficiently producing high quality annotations of breast tissue.
It is well known that state of the art algorithms in machine learning require vast amounts of data. Fields such as speech recognition [3], image recognition [4] and text processing [5] are able to deliver impressive performance with complex deep learning models because they have developed large corpora to support training of extremely high-dimensional models (e.g., billions of parameters). Other fields that do not have access to such data resources must rely on techniques in which existing models can be adapted to new datasets [6]. A preliminary version of this breast corpus release was tested in a pilot study using a baseline machine learning system, ResNet18 [7], that leverages several open-source Python tools.
The pilot corpus was divided into three sets: train, development, and evaluation. Portions of these slides were manually annotated [1] using the nine labels in Table 1 [8] to identify five to ten examples of pathological features on each slide. Not every pathological feature is annotated, meaning excluded areas can include focuses particular to these labels that are not used for training. A summary of the number of patches within each label is given in Table 2. To maintain a balanced training set, 1,000 patches of each label were used to train the machine learning model. Throughout all sets, only annotated patches were involved in model development.
The performance of this model in identifying all the patches in the evaluation set can be seen in the confusion matrix of classification accuracy in Table 3. The highest performing labels were background, 97% correct identification, and artifact, 76% correct identification. A correlation exists between labels with more than 6,000 development patches and accurate performance on the evaluation set. Additionally, these results indicated a need to further refine the annotation of invasive ductal carcinoma (“indc”), inflammation (“infl”), nonneoplastic features (“nneo”), normal (“norm”) and suspicious (“susp”).
This pilot experiment motivated changes to the corpus that will be discussed in detail in this poster presentation. To increase the accuracy of the machine learning model, we modified how we addressed underperforming labels. One common source of error arose with how non-background labels were converted into patches. Large areas of background within other labels were isolated within a patch resulting in connective tissue misrepresenting a non-background label. In response, the annotation overlay margins were revised to exclude benign connective tissue in non-background labels.
Corresponding patient reports and supporting immunohistochemical stains further guided annotation reviews. The microscopic diagnoses given by the primary pathologist in these reports detail the pathological findings within each tissue site, but not within each specific slide. The microscopic diagnoses informed revisions specifically targeting annotated regions classified as cancerous, ensuring that the labels “indc” and “dcis” were used only in situations where a micropathologist diagnosed it as such. Further differentiation of cancerous and precancerous labels, as well as the location of their focus on a slide, could be accomplished with supplemental immunohistochemically (IHC) stained slides. When distinguishing whether a focus is a nonneoplastic feature versus a cancerous growth, pathologists employ antigen targeting stains to the tissue in question to confirm the diagnosis. For example, a nonneoplastic feature of usual ductal hyperplasia will display diffuse staining for cytokeratin 5 (CK5) and no diffuse staining for estrogen receptor (ER), while a cancerous growth of ductal carcinoma in situ will have negative or focally positive staining for CK5 and diffuse staining for ER [9]. Many tissue samples contain cancerous and non-cancerous features with morphological overlaps that cause variability between annotators. The informative fields IHC slides provide could play an integral role in machine model pathology diagnostics.
Following the revisions made on all the annotations, a second experiment was run using ResNet18. Compared to the pilot study, an increase of model prediction accuracy was seen for the labels indc, infl, nneo, norm, and null. This increase is correlated with an increase in annotated area and annotation accuracy. Model performance in identifying the suspicious label decreased by 25% due to the decrease of 57% in the total annotated area described by this label. A summary of the model performance is given in Table 4, which shows the new prediction accuracy and the absolute change in error rate compared to Table 3.
The breast tissue subset we are developing includes 3,505 annotated breast pathology slides from 296 patients. The average size of a scanned SVS file is 363 MB. The annotations are stored in an XML format. A CSV version of the annotation file is also available which provides a flat, or simple, annotation that is easy for machine learning researchers to access and interface to their systems. Each patient is identified by an anonymized medical reference number. Within each patient’s directory, one or more sessions are identified, also anonymized to the first of the month in which the sample was taken. These sessions are broken into groupings of tissue taken on that date (in this case, breast tissue). A deidentified patient report stored as a flat text file is also available. Within these slides there are a total of 16,971 total annotated regions with an average of 4.84 annotations per slide. Among those annotations, 8,035 are non-cancerous (normal, background, null, and artifact,) 6,222 are carcinogenic signs (inflammation, nonneoplastic and suspicious,) and 2,714 are cancerous labels (ductal carcinoma in situ and invasive ductal carcinoma in situ.)
The individual patients are split up into three sets: train, development, and evaluation. Of the 74 cancerous patients, 20 were allotted for both the development and evaluation sets, while the remain 34 were allotted for train. The remaining 222 patients were split up to preserve the overall distribution of labels within the corpus. This was done in hope of creating control sets for comparable studies. Overall, the development and evaluation sets each have 80 patients, while the training set has 136 patients.
In a related component of this project, slides from the Fox Chase Cancer Center (FCCC) Biosample Repository (https://www.foxchase.org/research/facilities/genetic-research-facilities/biosample-repository -facility) are being digitized in addition to slides provided by Temple University Hospital. This data includes 18 different types of tissue including approximately 38.5% urinary tissue and 16.5% gynecological tissue. These slides and the metadata provided with them are already anonymized and include diagnoses in a spreadsheet with sample and patient ID. We plan to release over 13,000 unannotated slides from the FCCC Corpus simultaneously with v1.0.0 of TUDP. Details of this release will also be discussed in this poster.
Few digitally annotated databases of pathology samples like TUDP exist due to the extensive data collection and processing required. The breast corpus subset should be released by November 2021. By December 2021 we should also release the unannotated FCCC data. We are currently annotating urinary tract data as well. We expect to release about 5,600 processed TUH slides in this subset. We have an additional 53,000 unprocessed TUH slides digitized. Corpora of this size will stimulate the development of a new generation of deep learning technology. In clinical settings where resources are limited, an assistive diagnoses model could support pathologists’ workload and even help prioritize suspected cancerous cases.
ACKNOWLEDGMENTS
This material is supported by the National Science Foundation under grants nos. CNS-1726188 and 1925494. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
REFERENCES
[1] N. Shawki et al., “The Temple University Digital Pathology Corpus,” in Signal Processing in Medicine and Biology: Emerging Trends in Research and Applications, 1st ed., I. Obeid, I. Selesnick, and J. Picone, Eds. New York City, New York, USA: Springer, 2020, pp. 67 104. https://www.springer.com/gp/book/9783030368432.
[2] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning.” Major Research Instrumentation (MRI), Division of Computer and Network Systems, Award No. 1726188, January 1, 2018 – December 31, 2021. https://www. isip.piconepress.com/projects/nsf_dpath/.
[3] A. Gulati et al., “Conformer: Convolution-augmented Transformer for Speech Recognition,” in Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2020, pp. 5036-5040. https://doi.org/10.21437/interspeech.2020-3015.
[4] C.-J. Wu et al., “Machine Learning at Facebook: Understanding Inference at the Edge,” in Proceedings of the IEEE International Symposium on High Performance Computer Architecture (HPCA), 2019, pp. 331–344. https://ieeexplore.ieee.org/document/8675201.
[5] I. Caswell and B. Liang, “Recent Advances in Google Translate,” Google AI Blog: The latest from Google Research, 2020. [Online]. Available: https://ai.googleblog.com/2020/06/recent-advances-in-google-translate.html. [Accessed: 01-Aug-2021].
[6] V. Khalkhali, N. Shawki, V. Shah, M. Golmohammadi, I. Obeid, and J. Picone, “Low Latency Real-Time Seizure Detection Using Transfer Deep Learning,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2021, pp. 1 7. https://www.isip. piconepress.com/publications/conference_proceedings/2021/ieee_spmb/eeg_transfer_learning/.
[7] J. Picone, T. Farkas, I. Obeid, and Y. Persidsky, “MRI: High Performance Digital Pathology Using Big Data and Machine Learning,” Philadelphia, Pennsylvania, USA, 2020. https://www.isip.piconepress.com/publications/reports/2020/nsf/mri_dpath/.
[8] I. Hunt, S. Husain, J. Simons, I. Obeid, and J. Picone, “Recent Advances in the Temple University Digital Pathology Corpus,” in Proceedings of the IEEE Signal Processing in Medicine and Biology Symposium (SPMB), 2019, pp. 1–4. https://ieeexplore.ieee.org/document/9037859.
[9] A. P. Martinez, C. Cohen, K. Z. Hanley, and X. (Bill) Li, “Estrogen Receptor and Cytokeratin 5 Are Reliable Markers to Separate Usual Ductal Hyperplasia From Atypical Ductal Hyperplasia and Low-Grade Ductal Carcinoma In Situ,” Arch. Pathol. Lab. Med., vol. 140, no. 7, pp. 686–689, Apr. 2016. https://doi.org/10.5858/arpa.2015-0238-OA.
Recent work suggests that some aspects of lung nodule detection ability may relate to object recognition ability. However, this work only sampled radiological novices. Here, we further investigate whether object recognition ability predicts lung nodule detection ability (as measured by the Vanderbilt Chest Radiograph Test or VCRT), after controlling for experience and fluid intelligence, in a sample of radiologists and nonradiologists. We find that radiological experience accounts for approximately 50% of VCRT variance. After controlling for experience, fluid intelligence and object recognition ability account for an additional 15% of VCRT variance. These results suggest that while training is key in learning to detect nodules, given the same experience level, those with higher fluid intelligence and object recognition ability perform better. The recently proposed construct of visual object recognition ability may add unique information relative to general cognitive skills in assessing aptitude for a career in radiology.
Visual qualitative methodologies enhance the richness of data and makes participants experts on the object of interest. Visual data brings another dimension to the evaluation process, besides surveys and interviews, as well as depth and breadth to participants reactions to specific program activities. Visual data consists of images, such as photos, drawings, artwork, among others. Exploring a different approach to assess impact of an educational activity, an exercise was designed where participants were asked to take photos to document a site visit to an area impacted by a swarm of earthquakes in 2019. The exercise required taking five photos of either objects, persons, scenery, structures, or any other thing that captured their attention during the visit and write a reflective essay to answer three questions: 1) How do these photos represent your site visit experience? 2) Based on the content of your photos, write about what you learned, discovered, new knowledge acquired, emotions, changes in your way of thinking, etc., and 3) What did you learned or discovered from doing this exercise? Twenty-two undergraduate engineering and architecture students from the RISE-UP Program, enrolled in a curricular sequence in design and construction of resilient and sustainable structures, completed the exercise. Analyses of obtained data includes frequency of captured images and content analysis of reflective essays to determine instances where each of the four proposed learning objectives was present. Results show that across essays, 32% of the essays include text that demonstrate impact related to the first objective, 59% for the second, 73% for the third, and 86% for the fourth objective. Forty-five percent of essays included text considered relevant but not related to an objective. Personal, social, and career insights were categorized as unintended results. Photos taken by students represent what they considered relevant during the visit and also evidence the achievement of the proposed learning objectives. In general, three mayor categories emerged from the content in photos: 1) photos related to the design and construction of the structure and specific damage observed from earthquakes; 2) photos of classmates, professors, and group activities; and 3) other photos that do not share a theme. Both photos and essays demonstrate that the learning objectives were successfully achieved and encourage the use of visual data as an alternative for the evaluation of educational activities.
Chua, K.W., and Gauthier, I. The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants. Retrieved from https://par.nsf.gov/biblio/10201986. Journal of expertise .
Chua, K.W., & Gauthier, I. The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants. Journal of expertise, (). Retrieved from https://par.nsf.gov/biblio/10201986.
Chua, K.W., and Gauthier, I.
"The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants". Journal of expertise (). Country unknown/Code not available. https://par.nsf.gov/biblio/10201986.
@article{osti_10201986,
place = {Country unknown/Code not available},
title = {The Vanderbilt Holistic Processing Tests for Novel Objects: Validation in Novice Participants},
url = {https://par.nsf.gov/biblio/10201986},
abstractNote = {In recent work, the Vanderbilt Holistic Processing Tests for novel objects (VHPT-NOs), were used to show that holistic processing for artificial objects increased as a function of parametric variation of experience. Here, novices are tested on the VHPT-Nos to address two questions. First, does the test detect any level of holistic processing for novel objects in novices? Second, how is part matching performance on this test related to object recognition ability, as measured by the Novel Object Memory Test (NOMT)? In a high-powered study, we provide substantial evidence of no holistic processing on the VHPT-NO in novices, including for arguably facelike symmetrical Greebles. Evidence of no correlations between measures of holistic processing suggests that these indices can be considered free of influences from domain-general selective attention. In contrast, overall performance in part matching in the VHPT-NO shows shared variance across categories, which we postulate is related to object recognition. A second study provides direct evidence that part matching measures to a large extent the same ability as whole object learning on the NOMT. Our results suggest that any holistic processing measured in the VHPT-NOs will not be contaminated by domain-general effects and can be considered entirely due to experience with a category. The VHPT-NO will therefore be useful in further examination of how different aspects of experience contribute to the development of holistic processing.},
journal = {Journal of expertise},
author = {Chua, K.W. and Gauthier, I.},
editor = {null}
}
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