<p>This thesis investigates how gynaecology was established as a medical speciality in Sweden in the 1860s and onwards. Gender, power, professionalisation and the production of scientific knowledge are central themes. While previous research has shown that gynaecology as a discipline depends upon notions of Woman as radically different from Man, I show how this was manifested within Swedish gynaecology, an initially all male environment. Of special interest is institutionalisation, early career-paths and the development of therapy methods and theory. I argue that gynaecology reproduced and contributed to notions of sex-difference and a gender complementary way of thinking. </p><p>While gynaecology was formed as a surgically interventionist speciality with strong manly connotations, an education reform aiming at opening higher education to women was simultaneously discussed and eventually carried out during the 1860s and 70s. The advocates of this reform portrayed women as especially fit for becoming teachers and physicians, particularly treating women and children. Thus, two opposing gendered professional ideals operated. By focusing an elite group of early women physicians, I outline how the gynaecological construction of womanliness related to women physicians and how women physicians engaged with this notion: what strategies they used to enter a profession as manly as gynaecology had become; and how women gynaecologists engaged with their men colleagues’ therapeutic methods and views on patients and women.</p>

This thesis investigates how gynaecology was established as a medical speciality in Sweden in the 1860s and onwards. Gender, power, professionalisation and the production of scientific knowledge are central themes. While previous research has shown that gynaecology as a discipline depends upon notions of Woman as radically different from Man, I show how this was manifested within Swedish gynaecology, an initially all male environment. Of special interest is institutionalisation, early career-paths and the development of therapy methods and theory. I argue that gynaecology reproduced and contributed to notions of sex-difference and a gender complementary way of thinking. While gynaecology was formed as a surgically interventionist speciality with strong manly connotations, an education reform aiming at opening higher education to women was simultaneously discussed and eventually carried out during the 1860s and 70s. The advocates of this reform portrayed women as especially fit for becoming teachers and physicians, particularly treating women and children. Thus, two opposing gendered professional ideals operated. By focusing an elite group of early women physicians, I outline how the gynaecological construction of womanliness related to women physicians and how women physicians engaged with this notion: what strategies they used to enter a profession as manly as gynaecology had become; and how women gynaecologists engaged with their men colleagues’ therapeutic methods and views on patients and women.

Made available in DSpace on 2014-12-17T15:47:58Z (GMT). No. of bitstreams: 1 ItaloMSR_DISSERT.pdf: 4133389 bytes, checksum: 575496a3d8aa350df8e3e86992d9b27b (MD5) Previous issue date: 2011-03-02 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / The vascular segmentation is important in diagnosing vascular diseases like stroke and is hampered by noise in the image and very thin vessels that can pass unnoticed. One way to accomplish the segmentation is extracting the centerline of the vessel with height ridges, which uses the intensity as features for segmentation. This process can take from seconds to minutes, depending on the current technology employed. In order to accelerate the segmentation method proposed by Aylward [Aylward & Bullitt 2002] we have adapted it to run in parallel using CUDA architecture. The performance of the segmentation method running on GPU is compared to both the same method running on CPU and the original Aylward s method running also in CPU. The improvemente of the new method over the original one is twofold: the starting point for the segmentation process is not a single point in the blood vessel but a volume, thereby making it easier for the user to segment a region of interest, and; the overall gain method was 873 times faster running on GPU and 150 times more fast running on the CPU than the original CPU in Aylward / A segmenta??o vascular ? importante no diagn?stico de doen?as como o acidente vascular cerebral e ? dificultada por ru?dos na imagem e vasos muito finos que n?o s?o vistos. Uma maneira de realizar a segmenta??o ? extraindo a centerline do vaso com height ridges, que usa a intensidade como caracter?sticas para a segmenta??o. Este processo pode levar de segundos a minutos, dependendo da tecnologia atual empregada. O m?todo ? implementado em GPU, ou seja, ? executado de maneira paralela em placa gr?fica. O desempenho do m?todo de segmenta??o executado em GPU ? comparado com o mesmo m?todo em CPU e o m?todo original de Aylward em execu??o tamb?m na CPU. O melhoramento do novo m?todo sobre o original ? dupla. O ponto de partida para o processo de segmenta??o n?o ? um ?nico ponto no vaso sangu?neo, mas um volume, tornando assim mais f?cil para o usu?rio a sele??o de uma regi?o de interesse, e, o ganho do m?todo proposto foi 873 vezes mais r?pido sendo executado em GPU e 150 vezes mais r?pido sendo executado em CPU do que o original de Aylward em CPU

Made available in DSpace on 2016-03-15T19:37:58Z (GMT). No. of bitstreams: 1 RODRIGO FREITAS LIMA.pdf: 13768169 bytes, checksum: 153d5257eed9a0961aaeaac94e224f89 (MD5) Previous issue date: 2015-08-05 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / Paranasal sinus are important objects of study to rhinosinusitis diagnostic, having some papers related incidence between asthma and allergic rhinitis.Many applications can calculate to various parts of the human body, getting a CT scan or MRI input, and returning information about the region of interest observed as volume and area. The accumulated mucus in the sinuses is one of the areas of interest that have not yet been implemented methods for the calculation of volume and area. In the present scenario, the patient monitoring is done visually, depending largely on perception of the evaluator. Therefore, we seek to implement more accurate metrics to facilitate medical care to the patient and it can help prevent the worsening of rhinitis in a given patient, developing mechanisms of visual and numerical comparison, where it is possible observe the progress of treatment. This work contains a detailed study of how certain existing techniques, combined into one methodology can segment and calculate the accumulated mucus in the maxillary sinus. In addition to techniques such as Thresholding, Gaussian filter, Mathematical Morphology, Metallic Artifacts Reduction during processing and segmentation, MUNC and DTA to calculate the volume and area, and visualization techniques as the Marching Cubes, it was also necessary some adjustments in the algorithm for limit the region of interest where the thresholding combined with the gaussian filter has not been effective of retaining edges. The application will use two open source platforms, one for processing, ITK, and another for visualization, VTK. The results demonstrated that it is possible to perform segmentation and the calculation with the use of platforms as well as the methodology used is adequate to solve this problem. / Os seios paranasais são importantes objetos de estudo para o diagnóstico de rinossinusites, tendo alguns estudos relacionado a incidência de asma na fase adulta a quadros de rinite alérgica na infância. Muitas aplicações atendem a diversas partes do corpo humano, obtendo de entrada uma tomografia computadorizada ou ressonância magnética, e devolvendo, muitas vezes, números que dizem respeito ao objeto de interesse observado, como volume e área. O muco acumulado nos seios paranasais é uma das regiões de interesse que ainda não tiveram métodos implementados para o cálculo do volume e área. No cenário atual, o acompanhamento do paciente é feito de forma visual, dependendo muito da percepção do avaliador. Portanto, busca-se a implementação de métricas mais precisas para facilitar o acompanhamento médico ao paciente e ajudar na prevenção do agravamento de um quadro de rinite em um determinado paciente, criando mecanismos de comparação visual e numérica, onde é possível observar a evolução do tratamento. Este trabalho contém um estudo detalhado de como determinadas técnicas existentes, combinadas em uma metodologia, podem segmentar e calcular o muco acumulado nos seios paranasais maxilares. Além de técnicas como a Binarizacão, Filtro Gaussiano, Morfologia Matemática, Redução de Ruídos Metálico durante o processamento e segmentação, MUNC e DTA para o cálculo do volume e área, e técnicas de visualização como o Marching Cubes, foram necessários também ajustes no algoritmo para limitar a área segmentada onde a binarizacão combinada ao filtro não foi capaz de manter as bordas da região de interesse. A aplicação fará uso de duas plataformas de código livre, sendo uma para o processamento, ITK, e outra para visualização de imagens, VTK. Os resultados demonstraram que é possível realizar a segmentação e o cálculo com o uso das plataformas, bem como a metodologia empregada é adequada a resolução deste problema.

Hlavným zámerom tejto diplomovej práce bola automatická segmentácia lézií sklerózy multiplex na snímkoch MRI. V rámci práce boli otestované najnovšie metódy segmentácie s využitím hlbokých neurónových sietí a porovnané prístupy inicializácie váh sietí pomocou preneseného učenia (transfer learning) a samoriadeného učenia (self-supervised learning). Samotný problém automatickej segmentácie lézií sklerózy multiplex je veľmi náročný, a to primárne kvôli vysokej nevyváženosti datasetu (skeny mozgov zvyčajne obsahujú len malé množstvo poškodeného tkaniva). Ďalšou výzvou je manuálna anotácia týchto lézií, nakoľko dvaja rozdielni doktori môžu označiť iné časti mozgu ako poškodené a hodnota Dice Coefficient týchto anotácií je približne 0,86. Možnosť zjednodušenia procesu anotovania lézií automatizáciou by mohlo zlepšiť výpočet množstva lézií, čo by mohlo viesť k zlepšeniu diagnostiky individuálnych pacientov. Našim cieľom bolo navrhnutie dvoch techník využívajúcich transfer learning na predtrénovanie váh, ktoré by neskôr mohli zlepšiť výsledky terajších segmentačných modelov. Teoretická časť opisuje rozdelenie umelej inteligencie, strojového učenia a hlbokých neurónových sietí a ich využitie pri segmentácii obrazu. Následne je popísaná skleróza multiplex, jej typy, symptómy, diagnostika a liečba. Praktická časť začína predspracovaním dát. Najprv boli skeny mozgu upravené na rovnaké rozlíšenie s rovnakou veľkosťou voxelu. Dôvodom tejto úpravy bolo využitie troch odlišných datasetov, v ktorých boli skeny vytvárané rozličnými prístrojmi od rôznych výrobcov. Jeden dataset taktiež obsahoval lebku, a tak bolo nutné jej odstránenie pomocou nástroju FSL pre ponechanie samotného mozgu pacienta. Využívali sme 3D skeny (FLAIR, T1 a T2 modality), ktoré boli postupne rozdelené na individuálne 2D rezy a použité na vstup neurónovej siete s enkodér-dekodér architektúrou. Dataset na trénovanie obsahoval 6720 rezov s rozlíšením 192 x 192 pixelov (po odstránení rezov, ktorých maska neobsahovala žiadnu hodnotu). Využitá loss funkcia bola Combo loss (kombinácia Dice Loss s upravenou Cross-Entropy). Prvá metóda sa zameriavala na využitie predtrénovaných váh z ImageNet datasetu na enkodér U-Net architektúry so zamknutými váhami enkodéra, resp. bez zamknutia a následného porovnania s náhodnou inicializáciou váh. V tomto prípade sme použili len FLAIR modalitu. Transfer learning dokázalo zvýšiť sledovanú metriku z hodnoty približne 0,4 na 0,6. Rozdiel medzi zamknutými a nezamknutými váhami enkodéru sa pohyboval okolo 0,02. Druhá navrhnutá technika používala self-supervised kontext enkodér s Generative Adversarial Networks (GAN) na predtrénovanie váh. Táto sieť využívala všetky tri spomenuté modality aj s prázdnymi rezmi masiek (spolu 23040 obrázkov). Úlohou GAN siete bolo dotvoriť sken mozgu, ktorý bol prekrytý čiernou maskou v tvare šachovnice. Takto naučené váhy boli následne načítané do enkodéru na aplikáciu na náš segmentačný problém. Tento experiment nevykazoval lepšie výsledky, s hodnotou DSC 0,29 a 0,09 (nezamknuté a zamknuté váhy enkodéru). Prudké zníženie metriky mohlo byť spôsobené použitím predtrénovaných váh na vzdialených problémoch (segmentácia a self-supervised kontext enkodér), ako aj zložitosť úlohy kvôli nevyváženému datasetu.