626 research outputs found
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 cancerÃgenas 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 caracterÃsticas
radiómicas (incluindo a forma e tamanho, caracterÃsticas de primeira ordem, segunda
ordem e caracterÃsticas wavelets) foram extraÃdas dos gânglios linfáticos cervicais segmen-
tados em imagens de ressonância magnética. As informações clÃnicas incluÃam sexo, idade
e a presença do vÃrus do papiloma humano.
Três fluxos de trabalho baseados na radiómica e métodos de aprendizagem automá-
tica foram desenvolvidos. Na análise de caracterÃsticas radiómicas, foram avaliados três
limiares de correlação (0, 75, 0, 80, 0, 85) para remover as caracterÃsticas altamente corre-
lacionadas e cinco métodos de seleção de caracterÃsticas. Os melhores resultados foram
alcançados pelo terceiro fluxo de trabalho quando as variáveis clÃnicas foram incluÃdas 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 clÃnicas 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
- …