Motivation As a highly heterogeneous disease, clear cell renal cell carcinoma (ccRCC) has quite variable clinical behaviors. The prognostic biomarkers play a crucial role in stratifying patients suffering from ccRCC to avoid over- and under-treatment. Researches based on hand-crafted features and single-modal data have been widely conducted to predict the prognosis of ccRCC. However, these experience-dependent methods, neglecting the synergy among multimodal data, have limited capacity to perform accurate prediction. Inspired by complementary information among multimodal data and the successful application of convolutional neural networks (CNNs) in medical image analysis, a novel framework was proposed to improve prediction performance. Results We proposed a cross-modal feature-based integrative framework, in which deep features extracted from computed tomography/histopathological images by using CNNs were combined with eigengenes generated from functional genomic data, to construct a prognostic model for ccRCC. Results showed that our proposed model can stratify high- and low-risk subgroups with significant difference (P-value < 0.05) and outperform the predictive performance of those models based on single-modality features in the independent testing cohort [C-index, 0.808 (0.728–0.888)]. In addition, we also explored the relationship between deep image features and eigengenes, and make an attempt to explain deep image features from the view of genomic data. Notably, the integrative framework is available to the task of prognosis prediction of other cancer with matched multimodal data. Availability and implementation https://github.com/zhang-de-lab/zhang-lab? from=singlemessage Supplementary information Supplementary data are available at Bioinformatics online.
The endoscopy procedure has demonstrated great efficiency in detecting stomach lesions, with extensive numbers of endoscope images produced globally each day. The content-based gastric image retrieval (CBGIR) system has demonstrated substantial potential in gastric image analysis. Gastric precancerous diseases (GPD) have higher prevalence in gastric cancer patients. Thus, effective intervention is crucial at the GPD stage. In this paper, a CBGIR method is proposed using a modified ResNet-18 to generate binary hash codes for a rapid and accurate image retrieval process. We tested several popular models (AlexNet, VGGNet and ResNet), with ResNet-18 determined as the optimum option. Our proposed method was valued using a GPD data set, resulting in a classification accuracy of 96.21 ± 0.66% and a mean average precision of 0.927 ± 0.006, outperforming other state-of-art conventional methods. Furthermore, we constructed a Gastric-Map (GM) based on feature representations in order to visualize the retrieval results. This work has great auxiliary significance for endoscopists in terms of understanding the typical GPD characteristics and improving aided diagnosis.
Microscopic analysis of breast cell and tissue is a critical step in the definitive diagnosis of breast cancer. However, it's time-consuming and fatigable for histopathologists to find the diagnostic characteristic of cell and tissue in breast histopathological image through multiple magnification scannings. Many computer-aided studies, including traditional machine learning and deep learning approaches, have been conducted to efficiently assist histopathologists in making diagnostic decision. However, precision and complexity of such approaches remain challenging. In this work, we propose and evaluate a new framework, called multiscale context-cascaded ensemble framework (MsC 2 EF), to classify breast histopathological images. The model based on MsC 2 EF exhibits a higher precision than traditional machine learning. Meantime, it is more efficient and hardware-independent compared with deep learning approaches. The MsC 2 EF consists of the input, cascade, and decision layers. The input layer comprises a feature extractor and a spatial pyramid of image to execute feature input from coarse to fine scales. Four ensemble channels are stacked in a parallel manner as the cascade layer to select and transfer contextual feature iteratively and adaptively. For the decision layer, kernel fusion-based method is integrated to perform classification of breast histopathological image by fusing four different feature spaces. Our proposed method has been evaluated on an open dataset. The experimental result shows that MsC 2 EF obtains a good classification performance (Accuracy at patch level: 0.948±0.016; accuracy at patient level: 0.981±0.016), indicating its potential application to the classification of breast histopathologist images. INDEX TERMS MsC 2 EF, breast cancer, histopathological image, ensemble learning.
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