During the last few decades, many studies have been performed on the early detection of cancer using noninvasive or minimally invasive techniques in lieu of traditional excisional biopsy. Early detection can make an immense difference because cancer treatment is often simpler and more effective when diagnosed at an early stage. Cancer detecting methods may help physicians to diagnose cancer, to dissect the malignant region with a safe margin, and to evaluate the tumor bed after resection. In this paper, the advanced hyperspectral imaging system has been assessed using infrared wavelengths region for tumor detection. We were able to identify an appropriate wavelength region for cancer detection, spatially resolved images, and highlight the differences in reflectance properties of cancerous versus non‐cancerous tissues. The capability of this instrument was demonstrated by observing gastric tumors in 10 human subjects. The spectral signatures were extracted and evaluated in cancerous and non‐cancerous tissues. Processing means with the standard deviation of the spectral diagram, support vector machine, and first derivatives and integral of in spectral diagram were proposed to enhance and detect the cancerous regions. The first derivatives in spectral region between 1226–1251 nm and 1288–1370 nm were proposed as criteria that successfully distinguish between non‐cancerous and cancerous tissue. The results of this study will lead to advances in the optical diagnosis of cancer. (Cancer Sci 2011; 102: 852–857)
Color enhancement of multispectral images is useful to visualize the image's spectral features. Previously, a color enhancement method, which enhances the feature of a specified spectral band without changing the average color distribution, was proposed. However, sometimes the enhanced features are indiscernible or invisible, especially when the enhanced spectrum lies outside the visible range. In this paper, we extended the conventional method for more effective visualization of the spectral features both in visible range and non-visible range. In the proposed method, the user specifies both the spectral band for extracting the spectral feature and the color for visualization respectively, so that the spectral feature is enhanced with arbitrary color. The proposed color enhancement method was applied to different types of multispectral images where its effectiveness to visualize spectral features was verified.
Depression is a common, but serious mental disorder that affects people all over the world. Besides providing an easier way of diagnosing the disorder, a computer-aided automatic depression assessment system is demanded in order to reduce subjective bias in the diagnosis. We propose a multimodal fusion of speech and linguistic representation for depression detection. We train our model to infer the Patient Health Questionnaire (PHQ) score of subjects from AVEC 2019 DDS Challenge database, the E-DAIC corpus. For the speech modality, we use deep spectrum features extracted from a pretrained VGG-16 network and employ a Gated Convolutional Neural Network (GCNN) followed by a LSTM layer. For the textual embeddings, we extract BERT textual features and employ a Convolutional Neural Network (CNN) followed by a LSTM layer. We achieved a CCC score equivalent to 0.497 and 0.608 on the E-DAIC corpus development set using the unimodal speech and linguistic models respectively. We further combine the two modalities using a feature fusion approach in which we apply the last representation of each single modality model to a fully-connected layer in order to estimate the PHQ score. With this multimodal approach, it was possible to achieve the CCC score of 0.696 on the development set and 0.403 on the testing set of the E-DAIC corpus, which shows an absolute improvement of 0.283 points from the challenge baseline. CCS CONCEPTS • Computing methodologies → Machine learning; • Applied computing → Health care information systems.
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