Abstract：
:We propose the design of a teaching system named Interpretation and Diagnosis of Mammograms (INDIAM) for training students in the interpretation of mammograms and diagnosis of breast cancer. The proposed system integrates an illustrated tutorial on radiology of the breast, that is, mammography, which uses education techniques to guide the user (doctors, students, or researchers) through various concepts related to the diagnosis of breast cancer. The user can obtain informative text about specific subjects, access a library of bibliographic references, and retrieve cases from a mammographic database that are similar to a query case on hand. The information of each case stored in the mammographic database includes the radiological findings, the clinical history, the lifestyle of the patient, and complementary exams. The breast cancer tutorial is linked to a module that simulates the analysis and diagnosis of a mammogram. The tutorial incorporates tools for helping the user to evaluate his or her knowledge about a specific subject by using the education system or by simulating a diagnosis with appropriate feedback in case of error. The system also makes available digital image processing tools that allow the user to draw the contour of a lesion, the contour of the breast, or identify a cluster of calcifications in a given mammogram. The contours provided by the user are submitted to the system for evaluation. The teaching system is integrated with AMDI-An Indexed Atlas of Digital Mammograms-that includes case studies, e-learning, and research systems. All the resources are accessible via the Web.
journal_name
J Digit Imagingjournal_title
Journal of digital imagingauthors
Guliato D,Bôaventura RS,Maia MA,Rangayyan RM,Simedo MS,Macedo TAdoi
10.1007/s10278-008-9111-6subject
Has Abstractpub_date
2009-08-01 00:00:00pages
405-20issue
4eissn
0897-1889issn
1618-727Xjournal_volume
22pub_type
杂志文章abstract：:A computer algorithm for information retrieval from an electronic teaching file has been developed. This index enables the user to retrieve cases from a teaching file, based on the input of a combination of features. The algorithm is based on nearest neighbor analysis, and is programmed in the "C" language. A teaching...
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