Research in the medical imaging field using deep learning approaches has become progressively contingent. Scientific findings reveal that supervised deep learning methods’ performance heavily depends on training set size, which expert radiologists must manually annotate. The latter is quite a tiring and time-consuming task. Therefore, most of the freely accessible biomedical image datasets are small-sized. Furthermore, it is challenging to have big-sized medical image datasets due to privacy and legal issues. Consequently, not a small number of supervised deep learning models are prone to overfitting and cannot produce generalized output. One of the most popular methods to mitigate the issue above goes under the name of data augmentation. This technique helps increase training set size by utilizing various transformations and has been publicized to improve the model performance when tested on new data. This article surveyed different data augmentation techniques employed on mammogram images. The article aims to provide insights into basic and deep learning-based augmentation techniques.

/pdf/image-augmentation-techniques-for-mammogram-analysis-1vm2vbi9.pdf

Image Augmentation Techniques for Mammogram Analysis

Problems For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers. Aims A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD). Methods We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation. Conclusion Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.

https://www.techscience.com/CMES/online/detail/18940/pdf

A Survey of Convolutional Neural Network in Breast Cancer

Breast cancer is a prevalent form of cancer across the globe, and if it is not diagnosed at an early stage it can be life-threatening.In order to aid in its diagnosis, detection, and classification, computer-aided detection (CAD) systems are employed.You Only Look Once (YOLO)-based CAD algorithms have become very popular owing to their highly accurate results for object detection tasks in recent years.Therefore, the most popular YOLO models are implemented to compare the performance in mass detection with various experiments on the INbreast dataset.In addition, a YOLO model with an integrated Swin Transformer in its backbone is proposed for mass detection in mammography images within the study.The performance of YOLOv5 models and a transformer-based YOLO model is compared to that of each other and YOLOv3 and YOLOv4 models using images with different sizes on the INbreast dataset.The best results are obtained by the transformer-based YOLO model of YOLOv5 for 832 × 832 image size.In another experiment, we compared the default anchors against the anchors provided by the YOLOv5 autoanchor function before training and sawthat the anchors generated by the YOLOv5 autoanchor increased the success rates.Furthermore, various experiments were conducted to observe how data augmentation affects performance.Although a small amount of data was used in the study, high performance was obtained by YOLO algorithms, which are promising tools for cancer detection. 

A comparative study of YOLO models and a transformer-based YOLOv5 model for mass detection in mammograms

Today, the processing and analysis of mammograms is quite an important field of medical image processing. Small defects in images can lead to false conclusions. This is especially true when the distortion occurs due to minor malfunctions in the equipment. In the present work, an algorithm for eliminating a defect is proposed, which includes a change in intensity on a mammogram and deteriorations in the contrast of individual areas. The algorithm consists of three stages. The first is the defect identification stage. The second involves improvement and equalization of the contrasts of different parts of the image outside the defect. The third involves restoration of the defect area via a combination of interpolation and an artificial neural network. The mammogram obtained as a result of applying the algorithm shows significantly better image quality and does not contain distortions caused by changes in brightness of the pixels. The resulting images are evaluated using Blind/Referenceless Image Spatial Quality Evaluator (BRISQUE) and Naturalness Image Quality Evaluator (NIQE) metrics. In total, 98 radiomics features are extracted from the original and obtained images, and conclusions are drawn about the minimum changes in features between the original image and the image obtained by the proposed algorithm.

/pdf/elimination-of-defects-in-mammograms-caused-by-a-malfunction-14v0gqj0.pdf

Elimination of Defects in Mammograms Caused by a Malfunction of the Device Matrix

Breast cancer developing from a surgical scar is rare; this type of malignancy has been reported in only 12 cases to date. Herein, we report on a 52-year-old female who developed infiltrating ductal carcinoma in a surgical scar following excision of a benign mass. Two years previously, the patient underwent surgery and radiotherapy for invasive ductal carcinoma of the contralateral breast. The initial appearance of the scar was similar to fat necrosis; it was observed to be progressively shrinking on follow-up sonography. On the two year follow-up ultrasound, the appearance changed, an angular margin and vascularity at the periphery of the scar were noted. A biopsy and subsequent excision of the scar were performed; the diagnosis of infiltrating ductal carcinoma of the scar was confirmed.

Breast cancer from the excisional scar of a benign mass

азработка методики стандартизованного динамического УЗИ гипоэхогенного послеоперационного рубца молочной железы (МЖ) и оценка целесообразности ее использования с целью выявления рака молочной железы (РМЖ) в зоне рубцовых изменений. Материалы и методы. 263 пациентки в возрасте от 19 до 78 лет (средний возраст – 47,3±20,3 лет), перенесшие одно или несколько оперативных вмешательств на молочных железах (МЖ). Наблюдение начинали через 0,5-55 лет с момента оперативного вмешательства (средний период – 18 лет). Периодичность наблюдения с использованием оригинальной методики стандартизованного количественного динамического ультразвукового исследования (УЗИ) составляла 1 раз в 6 месяцев, минимальная длительность периода наблюдения для включения в анализ – 3 года. Результаты. В 89,7% случаев (236 пациенток) послеоперационный рубец паренхимы молочной железы (МЖ) имел высокую эхогенность, что позволяет сравнительно легко визуализировать на этом фоне гипоэхогенные очаги злокачественного роста. Однако в 10,3% случаев (27 пациенток) регистрировался гипоэхогенный рубец. В последней ситуации чувствительность традиционного УЗИ в выявлении злокачественных новообразований в зоне рубца оставляла 50%. Напротив, использование предложенной методики в данной ситуации повышало данный показатель до 100%. Обсуждение. Учитывая высокую распространенность оперативных вмешательств на молочных железах (МЖ) (в частности, выполняемых по поводу доброкачественной патологии), риск злокачественной трансформации послеоперационного рубца на МЖ впоследствии, а также ограничения и дороговизну альтернативных методов исследования (магнитно-резонансной томографии с контрастированием), использование предложенной методики динамического УЗИ рубца представляется оправданным. Выводы. Предложенная методика стандартизованного количественного динамического УЗИ с периодичностью исследований 1 раз в 6 месяцев обеспечивает повышение чувствительности УЗИ в выявлении рака молочной железы (РМЖ) в зоне рубца.

/pdf/quantitative-dynamic-ultrasound-in-the-follow-up-of-12ume8dy25.pdf

Quantitative dynamic ultrasound in the follow-up of hypoechoic breast post-surgical scar

/pdf/mammographic-and-echographic-changes-after-breast-surgery-21s9hxyjm1.pdf

Mammographic and echographic changes after breast surgery performed for benign lesions

Detection and classification of clusters of microcalcifications on mammographic images

In recent years, the traditional film technology of mammography has been replaced by digital technology, but comparative studies aimed at comparability of the size of changes when using these technologies are needed. The aim of the study was to assess the comparability of the size of changes corresponding to breast cancer in digital and film mammography, as well as the clinical significance of the dependencies identified. Material and methods. The study material was 27 sets of digital and film mammograms of patients with breast cancer, performed at intervals of no more than 2 months (digital mammograms – after film ones). The images were used to evaluate the same standardized neoplasm size. Results. The average size of changes corresponding to breast cancer on film mammograms was 16.96±8.44 mm, on digital ones – 15.63± 8.16 mm and 15.67±7.96 mm (p > 0.05). The range of variation in the difference in the neoplasm size according to film and digital mammography was 0-5 mm (0-30%). There were no cases of an increase in the neoplasm size on digital mammograms compared to film ones. In 37.04% (n = 10) of cases, the size of the changes in both types of mammography corresponded, in 29.63% (n = 8) understatement in digital mammography did not exceed 10%; in 25.93% (n = 7) of cases – 20%; in 7.40% (n = 2) – 30%. Underestimation of suspicious changes' sizes during digital mammography became clinically significant when their absolute size was less than 1 cm, when they became poorly visible against the background of fatty and fibrous-fatty parenchyma; as well as in situations when suspicious changes were indistinguishable from benign high-density zones. The use of a computer analysis system of mammograms in all cases ensured labeling changes corresponding to breast cancer. Conclusions. The sizes of changes corresponding to breast cancer (different from microcalcinates clusters, the size differences of which were not demonstrated) in film and digital mammography may differ by 0-30%, which does not allow using these techniques as interchangeable ones. Taking into account the possibility of underestimating the size of small changes, which makes them difficult to identify in the visual analysis of digital mammograms, it is advisable to use routinely a computer analysis system with subsequent targeted analysis of the labeled zones.

Revisiting comparability of the sizes of changes corresponding to breast cancer in film and digital mammography

Aim ― to describe the case of malignant adenomyoepithelioma of the breast and to review the corresponding literature, as well as to identify the specific mammographic and ultrasound signs of this malignancy. Material and methods. We report the case of breast tumor witсh histologic phenotype transformed from benign fibroadenoma to the borderline phylloid tumor and finally to the malignant adenomyoepithelioma. Results. At final examination the malignant adenomyoepithelioma showed the multi-nodular structure. Each node represented at mammography and ultrasound (US) the solid internal structure, oval shape and smooth contour. In case of suspicious non-calcified lesion found at mammography that has the solid internal structure and round or oval shape at US it is necessary to consider the following conditions during the differential diagnosis: low-grade invasive ductal carcinoma, mucinous, medillary, clear-cells carcinomas, invasive ductal carcinoma with necrosis or hemorrhage, low-grade adenosquamose carcinoma, metaplastic carcinoma, malignant adenomyoepithelioma and papillary carcinoma, as well as the following rare benign tumors: phylloid tumor, tubular adenoma, fat necrosis, intraductal papilloma, adenoma of the nipple, papilloma-related metaplastic tumors, ductal adenoma, sclerosing adenosis, complex screrozing lesion, and hemangioma. Conclusion. We found no specific imaging signs that allowed to identify the adenomyoepithelioma. This makes necessary to consider both benign and malignant adenomyoepithelioma during the differential diagnosis of round or oval shape solid lesion detected in mammography and/or ultrasound.

Malignant adenomyoepithelioma of the breast: a case report and the review of the literature

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