Expertise with encoding material has been shown to aid long-term memory for that material. It is not clear how relevant this expertise is for image memorability (e.g., radiologists' memory for radiographs), and how robust over time. In two studies, we tested scene memory using a standard long-term memory paradigm. One compared the performance of radiologists to naïve observers on two image sets, chest radiographs and everyday scenes, and the other radiologists' memory with immediate as opposed to delayed recognition tests using musculoskeletal radiographs and forest scenes. Radiologists' memory was better than novices for images of expertise but no different for everyday scenes. With the heterogeneity of image sets equated, radiologists' expertise with radiographs afforded them better memory for the musculoskeletal radiographs than forest scenes. Enhanced memory for images of expertise disappeared over time, resulting in chance level performance for both image sets after weeks of delay. Expertise with the material is important for visual memorability but not to the same extent as idiosyncratic detail and variability of the image set. Similar memory decline with time for images of expertise as for everyday scenes further suggests that extended familiarity with an image is not a robust factor for visual memorability.
Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4748147 | PMC |
| http://dx.doi.org/10.1117/1.JMI.3.1.011005 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.
Neuroradiology
December 2024
Dokuz Eylül Üniversitesi, Dokuz Eylül University Department of Radiology, 15 Temmuz Sağlık Sanat Yerleşkesi / İnciraltı, İzmir, 35340, Turkey.
Purpose: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.
Materials And Methods: In this retrospective multicenter study, brain DWI studies from January 2017 to April 2020 from Center 1, from June 2020 to December 2020 from Center 2, and from November 2019 to April 2020 from Center 3 were included. Four radiologists labeled images into five classes: anterior cerebral artery (ACA), middle cerebral artery (MCA), posterior circulation (PC), and watershed (WS) regions, as well as normal images.
HiFi-Syn: Hierarchical granularity discrimination for high-fidelity synthesis of MR images with structure preservation.
Med Image Anal
February 2025
Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Faculty of Medical Imaging Technology, College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Clinical Neuroscience Center, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China; National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, Shanghai Jiao Tong University, Shanghai, China. Electronic address:
Synthesizing medical images while preserving their structural information is crucial in medical research. In such scenarios, the preservation of anatomical content becomes especially important. Although recent advances have been made by incorporating instance-level information to guide translation, these methods overlook the spatial coherence of structural-level representation and the anatomical invariance of content during translation.
View Article and Find Full Text PDFTECRR: a benchmark dataset of radiological reports for BI-RADS classification with machine learning, deep learning, and large language model baselines.
BMC Med Inform Decis Mak
October 2024
School of Medicine, Tecnológico de Monterrey, Monterrey, 64849, Nuevo Leon, Mexico.
Article Synopsis
- * The dataset was originally in Spanish, translated into English, and preprocessed to ensure quality by removing duplicates and missing values, focusing on women with an average age of 53.
- * Various machine learning models, including KNN, SVM, and advanced models like BERT and BioGPT, were evaluated for their effectiveness in classifying BI-RADS categories using NLP techniques to extract meaningful information from the reports.
History Page: Leaders in MSK Radiology: Hooshang Taybi, 1919-2006.
Semin Musculoskelet Radiol
October 2024
International Skeletal Dysplasia Registry, Palo Alto, California.
This history page is dedicated to the memory and achievements of the pediatric radiologist Hooshang Taybi whose name is associated with the Rubinstein-Taybi syndrome, Taybi syndrome, and Taybi-Linder syndrome.
View Article and Find Full Text PDFDeep Learning Approaches for Brain Tumor Detection and Classification Using MRI Images (2020 to 2024): A Systematic Review.
J Imaging Inform Med
September 2024
Faculty of Sciences and Techniques, Department of Computer Science, L2IS Laboratory, Cadi Ayyad University, Marrakesh, Morocco.
Article Synopsis
- Brain tumors result from uncontrolled cell growth in the brain, leading to serious issues like memory loss and motor problems, making early diagnosis crucial for patient survival.
- Recent advancements in technology, particularly in computer-aided diagnosis and AI, are enhancing the accuracy of brain tumor detection using MRI images, while reducing the chances of misdiagnosis by busy radiologists.
- This paper reviews 60 studies from 2020 to January 2024, focusing on various deep learning methods, their limitations, and insights for future research, aiming to assist professionals and academics in the field of brain tumor classification and detection.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!