Colorectal cancer (CRC) is one of the most life-threatening malignancies. Colonoscopy pathology examination can identify cells early-stage colon tumors in small tissue image slices. But, such time-consuming and exhausting on high resolution images. In this paper, we present a new framework for colonoscopy whole slide (WSI) analysis, including lesion segmentation diagnosis. Our contains an improved U-shape network with VGG net as backbone, two schemes training inference, respectively (the...

Extubation failure is a complex and ongoing problem in the intensive care unit (ICU). It refers to patients who require re-intubation after extubation (namely disconnection from mechanical ventilation). In these patients, leads severe risks associated with increased mortalities, longer stay ICU also higher health costs. Many studies have been proposed analyze of identify possible factors or indices that may predict failure. However, used small number for limited their features several vital...

Many known supervised deep learning methods for medical image segmentation suffer an expensive burden of data annotation model training. Recently, few-shot were proposed to alleviate this burden, but such often showed poor adaptability the target tasks. By prudently introducing interactive into strategy, we develop a novel approach called Interactive Few-shot Learning (IFSL), which not only addresses models also tackles common issues methods. First, design new structure, Medical Prior-based...

Higher-resolution biopsy slice images reveal many details, which are widely used in medical practice. However, taking high-resolution is more costly than low-resolution ones. In this paper, we propose a joint framework containing novel transfer learning strategy and deep super-resolution to generate from The called SRFBN+ proposed by modifying state-of-the-art SRFBN. Specifically, the structure of feedback block SRFBN was modified be flexible. Besides, it challenging use typical strategies...

Drug response prediction (DRP) plays an important role in precision medicine (e.g. for cancer analysis and treatment). Recent advances deep learning algorithms make it possible to predict drug responses accurately based on genetic profiles. However, existing methods ignore the potential relationships among genes. In addition, similarity cell lines/drugs was rarely considered explicitly.We propose a novel DRP framework, called TGSA, better use of prior domain knowledge. TGSA consists Twin...

Keratoconus is one of the most severe corneal diseases, which difficult to detect at early stage (i.e., sub-clinical keratoconus) and possibly results in vision loss. In this paper, we propose a novel end-to-end deep learning approach, called KerNet, processes raw data Pentacam HR system (consisting five numerical matrices) keratoconus keratoconus. Specifically, convolutional neural network, containing branches as backbone with multi-level fusion architecture. The receive matrices separately...

To evaluate artificial intelligence (AI) models based on objective indices and raw corneal data from the Scheimpflug Pentacam HR system (OCULUS Optikgeräte GmbH, Wetzlar, Germany) for detection of clinically unaffected eyes in patients with asymmetric keratoconus (AKC) eyes.A total 1108 were enrolled, including 430 normal control subjects, 231 AKC, 447 (KC) patients. Eyes divided into a training set (664 eyes), test (222 eyes) validation eyes). AI built (XGBoost, LGBM, LR RF) entire...

Accurate and automatic segmentation of pulmonary nodules in 3D thoracic Computed Tomography (CT) images is great significance for Computer-Aided medical Diagnosis (CAD) lung cancer. Currently, this important task remains challenging lack the voxel-level annotation training strategies that balance target/background voxels CT images. In paper, a new region-based network, called Nodule-plus Region-based CNN, proposed to detect effectively while synchronously generating an instance mask every...

Convolutional Neural Networks (CNNs) have achieved remarkable results for many medical image segmentation tasks. However, segmenting small and polymorphous organs (e.g., pancreas) in 3D CT images is still highly challenging due to the complexity of such difficulties context information learning restricted by limited GPU memory. In this paper, we present a Fully Cascaded Framework pancreas images. We develop detection network (PancreasNet) regress locations regions, two different scales...

Interleukin-1 receptor associated kinase-1 (IRAK1) exhibits important roles in inflammation, infection, and autoimmune diseases; however, only a few inhibitors have been discovered. In this study, at first, discriminatory structure-based virtual screening (SBVS) was employed, but one active compound (compound 1, IC50 = 2.25 μM) identified. The low hit rate (2.63%) which derives from the weak power of docking among high-scored molecules observed our VS process for IRAK1 inhibitor....

Colorectal cancer is one of the most life-threatening malignancies, commonly occurring from intestinal polyps. Currently, clinical colonoscopy exam an effective way for early detection polyps and often conducted in real-time manner. But, analysis time-consuming suffers a high miss rate. In this paper, we develop novel stair-shape network (SSN) polyp segmentation images (not merely simple detection). Our new model much faster than U-Net, yet yields better performance segmentation. The first...

In clinical practice, medical image interpretation often involves multi-labeled classification, since the affected parts of a patient tend to present multiple symptoms or comorbidities. Recently, deep learning based frameworks have attained expertlevel performance on interpretation, which can be attributed partially large amounts accurate annotations. However, manually annotating massive images is impractical, while automatic annotation fast but imprecise (possibly introducing corrupted...

Accurate drug response prediction (DRP) is a crucial yet challenging task in precision medicine. This paper presents novel Attention-Guided Multi-omics Integration (AGMI) approach for DRP, which first constructs Multiedge Graph (MeG) each cell line, and then aggregates multi-omics features to predict using structure, called edge-aware Network (GeNet). For the time, our AGMI explores gene constraint based integration DRP with whole-genome GNNs. Empirical experiments on CCLE GDSC datasets show...

Accurate cervical lesion detection (CLD) methods using colposcopic images are highly demanded in computer-aided diagnosis (CAD) for automatic of High-grade Squamous Intraepithelial Lesions (HSIL). However, compared to natural scene images, the specific characteristics such as low contrast, visual similarity, and ambiguous boundaries, pose difficulties accurately locating HSIL regions also significantly impede performance improvement existing CLD approaches. To tackle these better capture...

As the world urbanizing rapidly, task of achieving smart growth in cities is becoming even more important.In this paper, we will propose a "Smart Growth Coefficient"(SGC) and analyze it by Multi-level Fuzzy Evaluation System.We first define SGC to measure extent which plan certain city accordant with principles growth.The greater SGC, better accordance.Dividing into multiple uniform grids(Gridding), grade each index indicator layer.For one index, ratio number grids graded score (Vi) total...

In recent years, with the popularity and development of Internet, information is spread more quickly.In this paper, based on a large amount data, we first consider "Information transfer capability parameters network" as an evaluation criterion to describe network, by virtue change rules establish prediction mechanism for parameter.Then introduce expansion mode, which "the accept not disseminate nodes "as situations that public interest opinion being changed through networks.Ultimately, model...