Students’ Views on Difficulties in Learning Histology

Garcı́a, Magdalena; Victory, Noemí; Navarro‐Sempere, Alicia; Segovia, Yolanda

doi:10.1002/ase.1838

Cited by 30 publications

(26 citation statements)

References 59 publications

(63 reference statements)

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“…The other 5 stated that the model is not a necessity but works as a complement that optimizes the class. Our results corroborate the idea presented by other studies (García et al, 2019;Gould et al, 2019) that state that graphic representations that are essentially assimilated by sight can also be perceived by touch and that a VI person depends on touch to form spatial concepts and create internal images (Melaku et al, 2016). When models were used as an aid to learning contribution, as in question 4, acceptance was unanimous among the investigated students, who considered this resource as a strong ally in the process of teaching and learning morphological disciplines.…”

Section: Resultssupporting

confidence: 89%

Histology for the Visually Impaired: A Study Applying Models at Universities in Spain and Brazil

et al. 2021

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This study aimed to develop models that reproduce histological slide images and evaluate them with blind and sighted students from an inclusion perspective. A questionnaire with questions about the ideal characteristics of a model was answered by visually impaired (VI) students. Based on the responses and optical microscopy images from histological atlases, nine models were constructed with standardized materials. The models were tested at different times with VI students and with students who see. Our results indicate that morphology teaching, according to the students themselves, should make use of extra resources, such as models. The ideal characteristics for model efficiency as a teaching tool were representative textures and reliefs. The models built were evaluated by blind students in Spain and sighted students in Brazil because, from an inclusive perspective, it is ideal that the same material can meet the demands of all types of students. The use of the developed models enhanced learning in both groups, even when combined with microscopes for sighted students. According to student opinions, the use of relief models can represent an important pedagogical strategy used to teach morphological. The importance of developing that materials was also highlighted from an inclusion perspective, as these materials can be used in mixed classes, all students together, in cooperative learning. This study aids morphological subject teachers who teach visually impaired students to know where to start on the path to an inclusive and pedagogically coherent learning process.

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Section: Resultssupporting

confidence: 89%

Histology for the Visually Impaired: A Study Applying Models at Universities in Spain and Brazil

et al. 2021

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“…Histology is the foundation of microscopic tissue evaluation and pathology diagnoses. [ 1 2 ] This cornerstone of medicine is an integral part of medical school curricula and serves as a pillar for pathology education. [ 2 ] Understanding the normal histologic architecture is key in building a microscopy-based diagnostic competency, and subtle variations in tissue morphology are challenging to master for new learners.…”

Section: Introductionmentioning

confidence: 99%

“…[ 1 2 ] This cornerstone of medicine is an integral part of medical school curricula and serves as a pillar for pathology education. [ 2 ] Understanding the normal histologic architecture is key in building a microscopy-based diagnostic competency, and subtle variations in tissue morphology are challenging to master for new learners. Unfortunately, teaching histology may require resources that are not always available in developing or underserved areas.…”

Section: Introductionmentioning

confidence: 99%

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Vali-Betts

Krause

Dubrovsky

et al. 2021

Journal of Pathology Informatics

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Aims: Histology, the microscopic study of normal tissues, is a crucial element of most medical curricula. Learning tools focused on histology are very important to learners who seek diagnostic competency within this important diagnostic arena. Recent developments in machine learning (ML) suggest that certain ML tools may be able to benefit this histology learning platform. Here, we aim to explore how one such tool based on a convolutional neural network, can be used to build a generalizable multi-classification model capable of classifying microscopic images of human tissue samples with the ultimate goal of providing a differential diagnosis (a list of look-alikes) for each entity. Methods: We obtained three institutional training datasets and one generalizability test dataset, each containing images of histologic tissues in 38 categories. Models were trained on data from single institutions, low quantity combinations of multiple institutions, and high quantity combinations of multiple institutions. Models were tested against withheld validation data, external institutional data, and generalizability test images obtained from Google image search. Performance was measured with macro and micro accuracy, sensitivity, specificity, and f1-score. Results: In this study, we were able to show that such a model's generalizability is dependent on both the training data source variety and the total number of training images used. Models which were trained on 760 images from only a single institution performed well on withheld internal data but poorly on external data (lower generalizability). Increasing data source diversity improved generalizability, even when decreasing data quantity: models trained on 684 images, but from three sources improved generalization accuracy between 4.05% and 18.59%. Maintaining this diversity and increasing the quantity of training images to 2280 further improved generalization accuracy between 16.51% and 32.79%. Conclusions: This pilot study highlights the significance of data diversity within such studies. As expected, optimal models are those that incorporate both diversity and quantity into their platforms.s

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“…In the image field shown in Figure 4b Teaching is another application for Minerva. Histology is challenging to teach in an undergraduate setting 41 and, in the case of medical students and residents, changes in curriculum have resulted in much less time available in front of a microscope. On-line collections of tissue images are a frequent substitute.…”

Section: Using Minerva Story To Explore a Human Tumormentioning

confidence: 99%

Online narrative guides for illuminating tissue atlas data and digital pathology images

Rashid

Yuan

Hoffer

et al. 2020

Preprint

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The recent introduction of highly multiplexed imaging of human tissues and tumors promises to fundamentally advance research in tissue biology and human disease. At the same time, histopathology in the clinical setting is undergoing a rapid transition to digital methods. Thus, repositories of imaging data from research and clinical specimens will soon join genomic databases as a means to systematically explore the molecular basis of disease. Even with recent advances in machine learning, experience in anatomic pathology has shown that there is no substitute for expert visual review, annotation, and description of image data. We review the ecosystem of software available for atlas and histopathology images and introduce a new Web-based software tool, Minerva Story, that addresses a critical unmet need. Minerva is an interpretative and interactive guide to complex images organized around guided analysis. We discuss how Minerva and similar software will be integrated into multi-omic browsers for data dissemination of future atlases.

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Students’ Views on Difficulties in Learning Histology

Cited by 30 publications

References 59 publications

Histology for the Visually Impaired: A Study Applying Models at Universities in Spain and Brazil

Histology for the Visually Impaired: A Study Applying Models at Universities in Spain and Brazil

Effects of Image Quantity and Image Source Variation on Machine Learning Histology Differential Diagnosis Models

Online narrative guides for illuminating tissue atlas data and digital pathology images

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