No abstract available.

No abstract available.
Toes*

No abstract available.
Toes*

No abstract available.
Myxoma*

No abstract available.
Darier Disease*

No abstract available.
Humans ; Mucocutaneous Lymph Node Syndrome* ; Onycholysis*

PURPOSE: Percutaneous vacuum-assisted breast biopsy (Mammotome, Fa. Ethicon Endo-Surgery Breast Care) is now commonly performed as the initial approach to diagnose and treat benign breast lesions. It can obviate the need for surgery in women with benign lesions and often lead to a one-stage surgical procedure when malignant lesions are diagnosed. The purpose of this study was to report the outcome of the Mammotome biopsy.. METHODS: We performed 902 cases of Mammotome biopsy (total 474 patients) to confirm the diagnosis and to excise the lesion between January 2003 and December 2006. Among all these cases, 888 cases with radiographically suspected benign lesions, below BI-RADS category 4, were followed up for more than 6 months and we analyzed clinical and pathologic results of these cases, retrospectively. Ultrasonographic follow-up examinations were performed and no malignant tumor or atypical ductal hyperplasia was included. RESULTS: The mean patient age was 42 (range: 17~76) years. The average size of lesion was 1.12 (range: 0.20~5.0) cm. In BI-RADS category, there were 450 (49.8%) C2 lesions, 158 (17.8%) C3 lesions and 251 (27.8%) C4 lesions. Histology revealed 506 cases (56.0%) of fibrocystic disease, 295 (32.7%) fibroadenomas, 11 (1.1%) intraductal papillomas, 16 cases (1.7%) of sclerosing adenosis, 21 cases (2.5%) of ductal epithelial hyperplasia, 18 cases (2.1%) of fat necrosis, 3 cases (0.3%) of gynecomastia, 18 cases (1.9%) of chronic inflammation. 824 lesions (92.8%) were completely removed, 39 cases (4.4%) of residual lesion and 25 cases (2.8%) of postoperative scar were reported. CONCLUSION: Mammotome biopsy is an effective diagnostic and therapeutic management of benign breast lesions with minimal-invasiveness and minimizes postoperative complications.
Biopsy ; Breast ; Cicatrix ; Fat Necrosis ; Female ; Fibroadenoma ; Follow-Up Studies ; Gynecomastia ; Humans ; Hyperplasia ; Inflammation ; Male ; Papilloma, Intraductal ; Postoperative Complications ; Retrospective Studies

C-arm fluoroscopy is a useful tool for interventional pain management. However, with the increasing use of C-arm fluoroscopy, the risk of accumulated radiation exposure is a significant concern for pain physicians. Therefore, efforts are needed to reduce radiation exposure. There are three types of radiation exposure sources: (1) the primary X-ray beam, (2) scattered radiation, and (3) leakage from the X-ray tube.The major radiation exposure risk for most medical staff members is scattered radiation, the amount of which is affected by many factors. Pain physicians can reduce their radiation exposure by use of several effective methods, which utilize the following main principles: reducing the exposure time, increasing the distance from the radiation source, and radiation shielding. Some methods reduce not only the pain physician’s but also the patient’s radiation exposure. Taking images with collimation and minimal use of magnification are ways to reduce the intensity of the primary X-ray beam and the amount of scattered radiation. It is also important to carefully select the C-arm fluoroscopy mode, such as pulsed mode or low-dose mode, for ensuring the physician’s and patient’s radiation safety. Pain physicians should practice these principles and also be aware of the annual permissible radiation dose as well as checking their radiation exposure. This article aimed to review the literature on radiation safety in relation to C-arm fluoroscopy and provide recommendations to pain physicians during C-arm fluoroscopy-guided interventional pain management.

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.

Deep learning has recently achieved remarkable results in the field of medical imaging. However, as a deep learning network becomes deeper to improve its performance, it becomes more difficult to interpret the processes within. This can especially be a critical problem in medical fields where diagnostic decisions are directly related to a patient's survival. In order to solve this, explainable artificial intelligence techniques are being widely studied, and an attention mechanism was developed as part of this approach. In this paper, attention techniques are divided into two types: post hoc attention, which aims to analyze a network that has already been trained, and trainable attention, which further improves network performance. Detailed comparisons of each method, examples of applications in medical imaging, and future perspectives will be covered.