Academic literature on the topic 'INbreast database'

In this study, we propose a tensor-based learning model to efficiently detect abnormalities on digital mammograms. Due to the fact that the availability of medical data is limited and often restricted by GDPR (general data protection regulation) compliance, the need for more sophisticated and less data-hungry approaches is urgent. Accordingly, our proposed artificial intelligence framework utilizes the canonical polyadic decomposition to decrease the trainable parameters of the wrapped Rank-R FNN model, leading to efficient learning using small amounts of data. Our model was evaluated on the open source digital mammographic database INBreast and compared with state-of-the-art models in this domain. The experimental results show that the proposed solution performs well in comparison with the other deep learning models, such as AlexNet and SqueezeNet, achieving 90% ± 4% accuracy and an F1 score of 84% ± 5%. Additionally, our framework tends to attain more robust performance with small numbers of data and is computationally lighter for inference purposes, due to the small number of trainable parameters.

Breast cancer’s high mortality rate is often linked to late diagnosis, with mammograms as key but sometimes limited tools in early detection. To enhance diagnostic accuracy and speed, this study introduces a novel computer-aided detection (CAD) ensemble system. This system incorporates advanced deep learning networks—EfficientNet, Xception, MobileNetV2, InceptionV3, and Resnet50—integrated via our innovative consensus-adaptive weighting (CAW) method. This method permits the dynamic adjustment of multiple deep networks, bolstering the system’s detection capabilities. Our approach also addresses a major challenge in pixel-level data annotation of faster R-CNNs, highlighted in a prominent previous study. Evaluations on various datasets, including the cropped DDSM (Digital Database for Screening Mammography), DDSM, and INbreast, demonstrated the system’s superior performance. In particular, our CAD system showed marked improvement on the cropped DDSM dataset, enhancing detection rates by approximately 1.59% and achieving an accuracy of 95.48%. This innovative system represents a significant advancement in early breast cancer detection, offering the potential for more precise and timely diagnosis, ultimately fostering improved patient outcomes.

BACKGROUND: Breast cancer is comprised of a series of complex and heterogeneous subtypes with difference in clinicalbehavior and outcomes. The immunohistochemistory defined subtypes have a predictive significance and prognostic value inbreast cancer. There are limited data regarding immunohistochemistory defined subtypes among Iraqi breast cancer patients. Theobjective of this study was to study the prevalence of immunohistochemistory defined subtypes and to identify their associationswith the risk of recurrence.PATIENTS AND METHODS: A study included 150 patients with breast cancer attending Baghdad oncology teaching hospitalbetween June 2019 and December 2019 (50 cases with negative recurrence history and 100 recurrence cases) for whom data includingage, stage, grade, histopathological type, date of recurrence for recurrence cases and date of last follow-up for cases withnegative recurrence history were collected. The breast cancer subtypes defined using immunohistochemical measures of hormonereceptors and human epidermal growth factor receptor 2 and classified into four major subtypes: luminal A, luminal B, HER2-positive, and triple negative. the association between these subtypes and the recurrent history was evaluated by Chi-squared test.RESULTS: The mean (±SD) age was 48.4 (±10.8) years. The immunohistochemistory defined subtypes of cancer was shown:luminal A in 79(52.7%)patients, 24(16%)patients had luminal B, 15(10%)patients had HER2 positive and 32(21.3%)patients hadtriple negative breast cancer. there were a significant association between immunohistochemistory defined subtypes and recurrenthistory (p=0.012).CONCLUSION: Tumor profiling using molecular subtypes is a promising agent to identify a cases at high risk of recurrence.

Computer-Aided Diagnosis (CADx) schemes have been proposed to serve as a supplementary image analysis tool in mammography. Experienced radiologists tend to be more assertive to such schemes in assisting their interpretation rather than solely relying on their ability to detect suspicious signals. This study focuses on a simplified version of a previously developed mammography CADx scheme, which was initially designed for digitized film, but is now specifically aimed at classifying breast nodules marked as regions of interest on digital images. This “driven” CADx scheme provides prompt indications regarding whether the selected nodule is deemed normal or suspicious. Its performance was evaluated through tests conducted on different mammograms sets – one with large number of images selected from DDSM database for training, testing and validation of classification parameters, and other comprising direct digital images from InBreast database. Remarkably, similar rates were observed for sensitivity, specificity and accuracy across these two sets (83%, 67% and 72%, respectively). The classification attributes were associated to contour, density and texture. Furthermore, a third test was conducted involving radiologists analyzing digital mammograms obtained from a specific full field digital mammography (FFDM) unit. Results showed that the Driven CADx scheme positively influenced the final diagnoses made by 3 radiologists, consistently increasing accuracy rates. This promising result allows establishing this software as a valuable tool for radiologists in the analysis of masses in digital mammography. The scheme can be implemented on any operating system, or even accessed online.