Who could benefit the most from using a computer-aided detection system in full-field digital mammography?

Vision-Transformer-Based Transfer Learning for Mammogram Classification

Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.

A review of the current state of the computer-aided diagnosis (CAD) systems for breast cancer diagnosis

Breast cancer is one of the most common cancers affecting females worldwide. Early detection and diagnosis of breast cancer may aid in timely treatment, reducing the mortality rate to a great extent. To diagnose breast cancer, computer-aided diagnosis (CAD) systems employ a variety of imaging modalities such as mammography, computerized tomography, magnetic resonance imaging, ultrasound, and histological imaging. CAD and breast-imaging specialists are in high demand for early detection and diagnosis. This system has the potential to enhance the partiality of traditional histopathological image analysis. This review aims to highlight the recent advancements and the current state of CAD systems for breast cancer detection using different modalities.

Impact of Concurrent Use of Artificial Intelligence Tools on Radiologists Reading Time: A Prospective Feasibility Study

RATIONAL AND OBJECTIVES

This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams.

MATERIALS AND METHODS

An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer. After each case radiologists answered questions regarding additional findings and perceived case overview. Reading times were recorded for 25 cases without and 20 cases with AI tool assistance for each reader. Differences in reading time with and without the AI tool were assessed using Welch's t-test for non-inferiority with non-inferiority limits defined as 100 seconds for the consultant and 200 seconds for the resident.

RESULTS

The mean reading time for the radiology resident was not significantly affected by the AI tool (without AI 370s vs with AI 437s; +67s 95% CI -28s to +163s, p = 0.16). The reading time for the radiology consultant was also not significantly affected by the AI tool (without AI 366s vs with AI 380s; +13s (95% CI - -57s to 84s, p = 0.70). The AI tool led to additional actionable findings in 5/40 (12.5%) studies and better overview in 18/20 (90%) of studies for the resident.

CONCLUSION

A PACS based implementation of an AI tool for concurrent reading of chest CT exams did not increase reading time with additional actionable findings made as well as a perceived better case overview for the radiology resident.

Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm

Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women's lives. The best feature optimization is performed to classify the results accurately. The CAD system's accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis methods, which are mainly based on the experiences of doctors. Therefore, the performance of such systems plays an important role in enhancing the quality of a diagnosing task. Although there have been the state-of-the art studies regarding this problem, which are based on handcrafted features, deep features, or the combination of the two, their performances are still limited. To overcome these problems, we propose an ultrasound image-based diagnosis of the malignant thyroid nodule method using artificial intelligence based on the analysis in both spatial and frequency domains. Additionally, we propose the use of weighted binary cross-entropy loss function for the training of deep convolutional neural networks to reduce the effects of unbalanced training samples of the target classes in the training data. Through our experiments with a popular open dataset, namely the thyroid digital image database (TDID), we confirm the superiority of our method compared to the state-of-the-art methods.

Artificial Intelligence-Based Thyroid Nodule Classification Using Information from Spatial and Frequency Domains

Image-based computer-aided diagnosis (CAD) systems have been developed to assist doctors in the diagnosis of thyroid cancer using ultrasound thyroid images. However, the performance of these systems is strongly dependent on the selection of detection and classification methods. Although there are previous researches on this topic, there is still room for enhancement of the classification accuracy of the existing methods. To address this issue, we propose an artificial intelligence-based method for enhancing the performance of the thyroid nodule classification system. Thus, we extract image features from ultrasound thyroid images in two domains: spatial domain based on deep learning, and frequency domain based on Fast Fourier transform (FFT). Using the extracted features, we perform a cascade classifier scheme for classifying the input thyroid images into either benign (negative) or malign (positive) cases. Through expensive experiments using a public dataset, the thyroid digital image database (TDID) dataset, we show that our proposed method outperforms the state-of-the-art methods and produces up-to-date classification results for the thyroid nodule classification problem.

A new approach for detecting abnormalities in mammograms using a computer-aided windowing system based on Otsu's method

Breast cancer is the most common cancer and the leading cause of cancer deaths in women worldwide. This study aimed to provide an automatic windowing method in mammograms, based on the principles of Otsu's thresholding function, to help radiologists more easily detect abnormalities on mammograms. A total of 322 mammographic images from the Mammographic Image Analysis Society (MIAS) database were used in the present study. The image background was removed based on Otsu's method. After selecting the threshold in the computer-aided windowing (CAW) system, the pixel values were kept larger than the threshold and displayed on a grayscale. A radiologist evaluated images randomly before and after CAW. Using CAW, the radiologist correctly diagnosed all healthy images (207 images). A total of 115 mammograms were evaluated to differentiate malignancy from benign masses. All 63 benign images were accurately diagnosed after using CAW. Moreover, of 52 malignant images, all were accurately recognized as malignant except one, which was recognized as benign. Therefore, specificity and sensitivity were significantly improved to 98% and 99.6%, respectively, and the area under the receiver operating characteristic (ROC) curve was calculated to be 0.99. The study showed that the use of CAW can potentially lead to quicker image assessment and improve the diagnostic accuracy of radiologists in differentiating between benign and malignant masses on mammograms.

Usefulness of grayscale inverted images in addition to standard images in digital mammography

BACKGROUND

Mammography is essential for early diagnosis of breast cancer, which is the most common type of cancer in females that is associated with a high mortality rate. We investigated whether evaluation of the grayscale inverted images of mammograms would aid in increasing the diagnostic sensitivity of the mammographic imaging technique.

METHODS

Our study included 636 mammograms of 159 women who had undergone digital mammography. Standard, grayscale inverted, and standard plus grayscale inverted images were sequentially examined three times, at 15-day intervals, for the presence or assessment of pathological changes in the skin, calcification, asymmetric density, mass lesions, structural distortions, and intramammary and axillary lymph nodes. To determine whether grayscale inverted image assessment improved detection rates, the results of the three assessment modes were compared using Cochran's Q test and the McNemar test (p < 0.05 was considered statistically significant).

RESULTS

The average age of 159 patients was 50.4 years (range, 35-80 years). There were significant differences among the three assessment modes with respect to calcification and intramammary lymph nodes (p < 0.05); however, no significant differences were observed for the detection of other parameters.

CONCLUSIONS

Assessment of grayscale inverted images in addition to standard images facilitates the detection of microcalcification.