Background: Students in biomedical disciplines require understanding of normal and abnormal microscopic appearances of human tissues (histology and histopathology). For this purpose, practical classes in these disciplines typically use virtual microscopy, viewing digitised whole slide images in web browsers. To enhance engagement, tools have been developed to enable individual or collaborative annotation of whole slide images within web browsers. To date, there have been no studies that have critically compared the impact on learning of individual and collaborative annotations on whole slide images.
Methods: Junior and senior students engaged in Pathology practical classes within Medical Science and Medicine programs participated in cross-over trials of individual and collaborative annotation activities. Students' understanding of microscopic morphology was compared using timed online quizzes, while students' perceptions of learning were evaluated using an online questionnaire.
Results: For senior medical students, collaborative annotation of whole slide images was superior for understanding key microscopic features when compared to individual annotation; whilst being at least equivalent to individual annotation for junior medical science students. Across cohorts, students agreed that the annotation activities provided a user-friendly learning environment that met their flexible learning needs, improved efficiency, provided useful feedback, and helped them to set learning priorities. Importantly, these activities were also perceived to enhance motivation and improve understanding.
Conclusion: Collaborative annotation improves understanding of microscopic morphology for students with sufficient background understanding of the discipline. These findings have implications for the deployment of annotation activities in biomedical curricula, and potentially for postgraduate training in Anatomical Pathology.
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http://dx.doi.org/10.1186/s12909-016-0831-x | DOI Listing |
Eur J Cancer
December 2024
Division of Digital Prevention, Diagnostics and Therapy Guidance, German Cancer Research Center (DKFZ), Heidelberg, Germany. Electronic address:
Purpose: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP) inhibitor maintenance therapy after response to platinum-based chemotherapy. HR status is currently analyzed via complex molecular tests. Predicting benefit from PARP inhibitors directly on histological whole slide images (WSIs) could be a fast and cheap alternative.
View Article and Find Full Text PDFMed Image Anal
December 2024
School of Biomedical Engineering & State Key Laboratory of Advanced Medical Materials and Devices, ShanghaiTech University, Shanghai, China; Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China; Shanghai Clinical Research and Trial Center, Shanghai, China. Electronic address:
Variations in hue and contrast are common in H&E-stained pathology images due to differences in slide preparation across various institutions. Such stain variations, while not affecting pathologists much in diagnosing the biopsy, pose significant challenges for computer-assisted diagnostic systems, leading to potential underdiagnosis or misdiagnosis, especially when stain differentiation introduces substantial heterogeneity across datasets from different sources. Traditional stain normalization methods, aimed at mitigating these issues, often require labor-intensive selection of appropriate templates, limiting their practicality and automation.
View Article and Find Full Text PDFCancer Res Commun
December 2024
Indian Institute of Technology Bombay, Mumbai, Maharashtra, India.
Intratumor heterogeneity (ITH) presents challenges for precision oncology, but methods for its spatial quantification, scalable at population levels, do not exist. Based on previous work showing that admixture of PAM50 subtype can be measured from bulk tissue using transcriptomic data, we trained a deep neural network (DNN) to quantify subtype ITH in Luminal A (LumA) breast cancer from routinely-stained whole slide images. We tested the hypothesis that subtype admixture detected in images was associated with tumor aggressiveness and adverse outcome.
View Article and Find Full Text PDFNPJ Digit Med
December 2024
School of Computing and Data Science, The University of Hong Kong, Hong Kong SAR, China.
Due to the large size and lack of fine-grained annotation, Whole Slide Images (WSIs) analysis is commonly approached as a Multiple Instance Learning (MIL) problem. However, previous studies only learn from training data, posing a stark contrast to how human clinicians teach each other and reason about histopathologic entities and factors. Here, we present a novel knowledge concept-based MIL framework, named ConcepPath, to fill this gap.
View Article and Find Full Text PDFNat Commun
December 2024
Beijing National Laboratory for Condensed Matter Physics and Institute of Physics, Chinese Academy of Sciences, Beijing, China.
Recent advances have uncovered an exotic sliding ferroelectric mechanism, which endows to design atomically thin ferroelectrics from non-ferroelectric parent monolayers. Although notable progress has been witnessed in understanding the fundamental properties, functional devices based on sliding ferroelectrics remain elusive. Here, we demonstrate the rewritable, non-volatile memories at room-temperature with a two-dimensional (2D) sliding ferroelectric semiconductor of rhombohedral-stacked bilayer MoS.
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