Prediction of Unknown Primary Carcinoma in Head and Neck Cancer using Radiomics

The unknown primary carcinoma in head and neck cancer (HNC) is a rare disease in which cancer cells spread to lymph nodes in the upper neck, but the place where it began is unknown. The diagnostic protocol to identify the primary tumour location is challenging and invasive. In return, radiomics, a quick, low-cost, non-invasive and repeatable method, has been demonstrated in this dissertation to be a valuable tool for diagnosing the primary tumour location in these patients. The dataset analysed comprises 400 HNC patients with unknown primary carcinoma from the National Cancer Institution of Milano. The primary tumour sites already diag- nosed were Hypopharynx and Larynx (HL; n = 38), Oral Cavity (OC; n = 63), Oropharynx (OPh; n = 162) and Nasopharynx (NPh; n = 137). In total, 265 radiomic features (includ- ing shape and size, first-order, second-order, and wavelet features) were extracted from the cervical lymph nodes segmented in MRI images. The clinical information included sex, age and HPV status. Three workflows based on radiomics and machine learning methods were developed in this project. In radiomic features analysis, three correlation thresholds (0.75, 0.80, 0.85) to remove the highly correlated features and five distinctive feature selection meth- ods were assessed. The best results were achieved by the third workflow when clinical information was included in the feature set selected by Sequential Backward Selection and trained with a Linear Support Vector Machine classifier. The highest accuracies ob- tained in predicting each tumour location were 78.8% for HL, 75.4% for OC, 71.5% for OPh and 95.2% for NPh. The percentage of unclassified patients was 0.5%. The outcomes indicate that radiomics with machine learning techniques and clinical information hold the potential to predict the primary tumour site accurately.O carcinoma de tumor primário desconhecido no cancro da cabeça e do pescoço (CCP) é uma doença rara em que as células cancerígenas se espalham para os gânglios linfáticos do pescoço, mas o local onde o tumor se inicia é desconhecido. O protocolo padrão para diagnosticar o tumor primário é desafiador e invasivo. Em contrapartida, a radiómica, sendo um método rápido, de baixo custo e não invasivo, demonstrou-se neste projeto ser uma ferramenta valiosa para a localização do tumor primário nesses pacientes. O conjunto de dados analisado inclui 400 pacientes do CCP com carcinoma primá- rio desconhecido do Instituto Nacional do Cancro de Milão. Os tumores primários, já diagnosticados, foram Hipofaringe e Laringe (HL; n = 38), Cavidade Oral (CO; n = 63), Orofaringe (Oro; n = 162) e Nasofaringe (Naso; n = 137). No total, 265 características radiómicas (incluindo a forma e tamanho, características de primeira ordem, segunda ordem e características wavelets) foram extraídas dos gânglios linfáticos cervicais segmen- tados em imagens de ressonância magnética. As informações clínicas incluíam sexo, idade e a presença do vírus do papiloma humano. Três fluxos de trabalho baseados na radiómica e métodos de aprendizagem automá- tica foram desenvolvidos. Na análise de características radiómicas, foram avaliados três limiares de correlação (0, 75, 0, 80, 0, 85) para remover as características altamente corre- lacionadas e cinco métodos de seleção de características. Os melhores resultados foram alcançados pelo terceiro fluxo de trabalho quando as variáveis clínicas foram incluídas no modelo treinado (Máquina de Vetores de Suporte Linear). A precisão obtida na predição do tumor HL foi de 78, 8%, na da CO foi de 75, 4%, na do Oro foi de 71, 5% e na predição do tumor Naso foi de 95, 2%. A percentagem de pacientes não classificados foi de 0, 5%. Os resultados indicam que a radiómica em conjunto com métodos de aprendizagem automática e informações clínicas têm potencial para prever com precisão o local do tumor primário em pacientes com carcinoma de tumor primário oculto no CCP

TET-GAN: Text Effects Transfer via Stylization and Destylization

Text effects transfer technology automatically makes the text dramatically more impressive. However, previous style transfer methods either study the model for general style, which cannot handle the highly-structured text effects along the glyph, or require manual design of subtle matching criteria for text effects. In this paper, we focus on the use of the powerful representation abilities of deep neural features for text effects transfer. For this purpose, we propose a novel Texture Effects Transfer GAN (TET-GAN), which consists of a stylization subnetwork and a destylization subnetwork. The key idea is to train our network to accomplish both the objective of style transfer and style removal, so that it can learn to disentangle and recombine the content and style features of text effects images. To support the training of our network, we propose a new text effects dataset with as much as 64 professionally designed styles on 837 characters. We show that the disentangled feature representations enable us to transfer or remove all these styles on arbitrary glyphs using one network. Furthermore, the flexible network design empowers TET-GAN to efficiently extend to a new text style via one-shot learning where only one example is required. We demonstrate the superiority of the proposed method in generating high-quality stylized text over the state-of-the-art methods.Comment: Accepted by AAAI 2019. Code and dataset will be available at http://www.icst.pku.edu.cn/struct/Projects/TETGAN.htm

Demystifying Neural Style Transfer

Neural Style Transfer has recently demonstrated very exciting results which catches eyes in both academia and industry. Despite the amazing results, the principle of neural style transfer, especially why the Gram matrices could represent style remains unclear. In this paper, we propose a novel interpretation of neural style transfer by treating it as a domain adaptation problem. Specifically, we theoretically show that matching the Gram matrices of feature maps is equivalent to minimize the Maximum Mean Discrepancy (MMD) with the second order polynomial kernel. Thus, we argue that the essence of neural style transfer is to match the feature distributions between the style images and the generated images. To further support our standpoint, we experiment with several other distribution alignment methods, and achieve appealing results. We believe this novel interpretation connects these two important research fields, and could enlighten future researches.Comment: Accepted by IJCAI 201