Tibial stress injuries : location, severity, and classification in magnetic resonance imaging examination

Purpose: To describe and illustrate the spectrum of magnetic resonance imaging (MRI) findings of tibial stress injuries (TSI) and propose a simplified classification system. Material and methods: Retrospective analysis of MRI exams of 44 patients with clinical suspicion of unilateral or bilateral TSI, using a modified classification system to evaluate the intensity and location of soft-tissue changes and bone changes. Results: Most of the patients were young athletic men diagnosed in late stage of TSI. Changes were predominantly found in the middle and distal parts of tibias along medial and posterior borders. Conclusions: TSI may be suspected in young, healthy patients with exertional lower leg pain. MRI is the only diagnostic method to visualise early oedematic signs of TSI. Knowledge of typical locations of TSI can be helpful in proper diagnosis before its evolution to stress fracture

Przegląd metod uczenia głębokiego w wykrywaniu małych i bardzo małych obiektów

In recent years, thanks to the development of Deep Learning methods, there has been significant progress in object detection and other computer vision tasks. While generic object detection is becoming less of an issue for modern algorithms, with the Average Precision for medium and large objects in the COCO dataset approaching 70 and 80 percent, respectively, small object detection still remains an unsolved problem. Limited appearance information, blurring, and low signal-to-noise ratio cause state-of-the-art general detectors to fail when applied to small objects. Traditional feature extractors rely on downsampling, which can cause the smallest objects to disappear, and standard anchor assignment methods have proven to be less effective when used to detect low-pixel instances. In this work, we perform an exhaustive review of the literature related to small and tiny object detection. We aggregate the definitions of small and tiny objects, distinguish between small absolute and small relative sizes, and highlight their challenges. We comprehensively discuss datasets, metrics, and methods dedicated to small and tiny objects, and finally, we make a quantitative comparison on three publicly available datasets.W ostatnich latach, dzięki rozwojowi metod uczenia głębokiego, dokonano znacznego postępu w detekcji obiektów i innych zadaniach widzenia maszynowego. Mimo że ogólne wykrywanie obiektów staje się coraz mniej problematyczne dla nowoczesnych algorytmów, a średnia precyzja dla średnich i dużych instancji w zbiorze COCO zbliża się odpowiednio do 70 i 80 procent, wykrywanie małych obiektów pozostaje nierozwiązanym problemem. Ograniczone informacje o wyglądzie, rozmycia i niski stosunek sygnału do szumu powodują, że najnowocześniejsze detektory zawodzą, gdy są stosowane do małych obiektów. Tradycyjne ekstraktory cech opierają się na próbkowaniu w dół, które może powodować zanikanie najmniejszych obiektów, a standardowe metody przypisania kotwic są mniej skuteczne w wykrywaniu instancji o małej liczbie pikseli. W niniejszej pracy dokonujemy wyczerpującego przeglądu literatury dotyczącej wykrywania małych i bardzo małych obiektów. Przedstawiamy definicje, rozróżniamy małe wymiary bezwzględne i względne oraz podkreślamy związane z nimi wyzwania. Kompleksowo omawiamy zbiory danych, metryki i metody, a na koniec dokonujemy porównania ilościowego na trzech publicznie dostępnych zbiorach danych

The Frequency of Use and Harm Perception of Heated Tobacco Products (HTPs): The 2019 Cross-Sectional Survey among Medical Students from Poland

Heated tobacco products (HTPs) are devices for generating a nicotine aerosol by heating the tobacco sticks. This study aimed to assess (1) the prevalence of HTP and tobacco cigarette usage among medical students, (2) to characterize smoking habits and (3) to assess students’ awareness and opinions about HTPs. A cross-sectional survey on the frequency and attitudes toward cigarettes, e-cigarettes and HTP use was performed between 2019–2020 at the Medical University of Silesia in Katowice (Poland). The data were obtained from 1344 students aged 21.8 ± 1.9 years (response rate: 66.9%). Current traditional tobacco use was 13.2%, e-cigarettes use 3.5%, and HTP use 2.8% of students. Duration of use was shorter among HTPs users comparing to cigarette smokers (p < 0.001) although the number of tobacco sticks used daily was similar (p = 0.1). Almost 30% of respondents have ever tried HTPs. HTPs were considered safe by 5.3% of respondents (43.2% of HTP users vs. 3.9% of non-HTP users, p < 0.001). HTP users were more likely to report that heating tobacco is not addictive (odds ratio (OR) = 8.9, 95% confidence interval (CI): 1.8–45.8) and disagreed with a public ban on HTP use (OR = 4.9, 95%CI: 2.5–9.8). Among students, HTP use was less popular than cigarette smoking, but awareness of their presence is widespread