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A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

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A Precision Diagnostic Framework of Renal Cell Carcinoma on ...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Ruonan Zhang Tieliang Gong Xinrui Bao Zeyu Gao Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China Library of Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA the First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665429825

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading. The results show that our framework has achieved pathologist-level accuracy in diagnosis, can generate pathology reports with tumor indicators, and provide pathologists with interpretable auxiliary diagnoses

关键词： Deep learning Pathology Visualization Pipelines Neural networks Ontologies Prostate cancer

Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image

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arXiv 2021年

作者： Gao, Zeyu Hong, Bangyang Zhang, Xianli Li, Yang Jia, Chang Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an Shaanxi710049 China

Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the papillary architecture, yet some subtle differences, including cellular and cell-layer level patterns. However, the cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as input and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a gradeembedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists are invited to selected 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin. © 2021, CC BY.

关键词： Formal languages

Nuclei grading of clear cell renal cell carcinoma in histopathological image by composite high-resolution network

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arXiv 2021年

作者： Gao, Zeyu Shi, Jiangbo Zhang, Xianli Li, Yang Zhang, Haichuan Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an710049 China

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained classification of nuclei to two cross-category classification tasks, based on two high-resolution feature extractors (HRFEs) which are proposed for learning these two tasks. The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited for the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset. © 2021, CC BY.

关键词： Grading

Universal deep network for steganalysis of color image based on channel representation

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arXiv 2021年

作者： Wei, Kangkang Luo, Weiqi Tan, Shunquan Huang, Jiwu The Guangdong Key Lab of Information Security Technology School of Computer Science and Engineering Sun Yat-Sen University Guangzhou510006 China The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Guangdong Key Laboratory of Intelligent Information Processing Shenzhen Key Laboratory of Media Security National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China

Up to now, most existing steganalytic methods are designed for grayscale images, and they are not suitable for color images that are widely used in current social networks. In this paper, we design a universal color image steganalysis network (called UCNet) in spatial and JPEG domains. The proposed method includes preprocessing, convolutional, and classification modules. To preserve the steganographic artifacts in each color channel, in preprocessing module, we firstly separate the input image into three channels according to the corresponding embedding spaces (i.e. RGB for spatial steganography and YCbCr for JPEG steganography), and then extract the image residuals with 62 fixed high-pass filters, finally concatenate all truncated residuals for subsequent analysis rather than adding them together with normal convolution like existing CNN-based steganalyzers. To accelerate the network convergence and effectively reduce the number of parameters, in convolutional module, we carefully design three types of layers with different shortcut connections and group convolution structures to further learn high-level steganalytic features. In classification module, we employ a global average pooling and fully connected layer for classification. We conduct extensive experiments on ALASKA II to demonstrate that the proposed method can achieve state-of-the-art results compared with the modern CNN-based steganalyzers (e.g., SRNet and J-YeNet) in both spatial and JPEG domains, while keeping relatively few memory requirements and training time. Furthermore, we also provide necessary descriptions and many ablation experiments to verify the rationality of the network design. Copyright © 2021, The Authors. All rights reserved.

关键词： Steganography

Matching Ontologies Through Evolutionary Algorithm with Context-Based Reasoning 6th

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Matching Ontologies Through Evolutionary Algorithm with Cont...

6th International Conference on Advanced Machine Learning Technologies and Applications, AMLTA 2021

作者： Yang, Chaofan Xue, Xingsi Yue, Cai Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fuzhou Fujian350118 China Intelligent Information Processing Research Center Fujian University of Technology Fuzhou Fujian350118 China College of Information Science and Engineering Fujian University of Technology Fuzhou Fujian350118 China Fujian Key Lab for Automotive Electronics and Electric Drive Fujian University of Technology Fuzhou Fujian350118 China Guangxi Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin Guangxi541004 China School of Information and Communication Guilin University of Electronic Technology Guilin Guangxi541004 China

ISBN: (纸本)9783030697167

Ontology matching can solve the heterogeneity problem between two ontologies, and EA represents a state-of-the-art technique for matching ontologies. However, there are two defects concerning the EA-based ontology matching technique: (1) a reference alignment between two ontologies to be matched is required in advance;(2) the confidence of entity similarity measure is low computational complexity of measuring the similarity value is high. To overcome these drawbacks, in this paper, an Evolutionary Algorithm with Context-based Reasoning method (EA-CR) is proposed, where: (1) an approximate metric without the reference alignment is utilized for evaluating the alignment’s quality;(2) a Context-based Reasoning method is presented to distinguish the heterogeneous entities. The experimental results show that the proposed approach is effective. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Ontology

COVID-19 screening on chest X-ray images using deep learning based anomaly detection

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arXiv 2020年

作者： Zhang, Jianpeng Xie, Yutong Li, Yi Shen, Chunhua Xia, Yong National Engineering Lab for Integrated Aerospace-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University China University of Adelaide Australia JF Healthcare Crop. China

Coronaviruses are important human and animal pathogens. To date the novel COVID-19 coronavirus is rapidly spreading worldwide and subsequently threatening health of billions of humans. Clinical studies have shown that most COVID-19 patients suffer from the lung infection. Although chest CT has been shown to be an effective imaging technique for lung-related disease diagnosis, chest X-ray is more widely available due to its faster imaging time and considerably lower cost than CT. Deep learning, one of the most successful AI techniques, is an effective means to assist radiologists to analyze the vast amount of chest X-ray images, which can be critical for efficient and reliable COVID-19 screening. In this work, we aim to develop a new deep anomaly detection model for fast, reliable screening. To evaluate the model performance, we have collected 100 chest X-ray images of 70 patients confirmed with COVID-19 from the Github repository1. To facilitate deep learning, more data are needed. Thus, we have also collected 1431 additional chest X-ray images confirmed as other pneumonia of 1008 patients from the public ChestX-ray14 dataset. Our initial experimental results show that the model developed here can reliably detect 96.00% COVID-19 cases (sensitivity being 96.00%) and 70.65% non-COVID-19 cases (specificity being 70.65%) when evaluated on 1531 X-ray images with two splits of the dataset, offering a promise of our proposed model for reliable COVID-19 screening of chest X-ray images. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Correction: FragGen: towards 3D geometry reliable fragment-based molecular generation

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Chemical science 2025年第3期16卷 1467页

作者： Odin Zhang Yufei Huang Shicheng Chen Mengyao Yu Xujun Zhang Haitao Lin Yundian Zeng Mingyang Wang Zhenxing Wu Huifeng Zhao Zaixi Zhang Chenqing Hua Yu Kang Sunliang Cui Peichen Pan Chang-Yu Hsieh Tingjun Hou College of Pharmaceutical Sciences Zhejiang University Hangzhou 310058 Zhejiang China slcui@*** panpeichen@*** kimhsieh@*** tingjunhou@***. Zhejiang University Hangzhou 310058 Zhejiang China. Anhui Province Key Lab of Big Data Analysis and Application University of Science and Technology of China Hefei Anhui China. Montreal Institute for Learning Algorithms McGill University Montreal QC Canada.

[This corrects the article DOI: 10.1039/D4SC04620J.].

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A novel explainable AI framework for medical image classification integrating statistical, visual, and rule-based methods

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Medical Image Analysis 2025年 105卷

作者： Naeem Ullah Florentina Guzmán-Aroca Francisco Martínez-Álvarez Ivanoe De Falco Giovanna Sannino Department of Electrical Engineering and Information Technology University of Naples Federico II via Claudio 21 Naples 80125 Italy Department of Radiology Hospital Clínico Universitario “Virgen de la Arrixaca” Carretera Cartagena-Madrid s/n Murcia 30120 Spain Data Science and Big Data Lab Pablo de Olavide University Ctra. de Utrera 1 Seville 41013 Spain Institute of High Performance and Networking (ICAR) National Research Council of Italy (CNR) via P.Castellino 111 Naples 80131 Italy

Artificial intelligence and deep learning are powerful tools for extracting knowledge from large datasets, particularly in healthcare. However, their black-box nature raises interpretability concerns, especially in high-stakes applications. Existing eXplainable Artificial Intelligence methods often focus solely on visualization or rule-based explanations, limiting interpretability’s depth and clarity. This work proposes a novel explainable AI method specifically designed for medical image analysis, integrating statistical, visual, and rule-based explanations to improve transparency in deep learning models. Statistical features are derived from deep features extracted using a custom Mobilenetv2 model. A two-step feature selection method – zero-based filtering with mutual importance selection – ranks and refines these features. Decision tree and RuleFit models are employed to classify data and extract human-readable rules. Additionally, a novel statistical feature map overlay visualization generates heatmap-like representations of three key statistical measures (mean, skewness, and entropy), providing both localized and quantifiable visual explanations of model decisions. The proposed method has been validated on five medical imaging datasets – COVID-19 radiography, ultrasound breast cancer, brain tumor magnetic resonance imaging, lung and colon cancer histopathological, and glaucoma images – with results confirmed by medical experts, demonstrating its effectiveness in enhancing interpretability for medical image classification tasks.

关键词： Explainable artificial intelligence Medical image classification Feature engineering Rule-based interpretability

BaT: Beat-aligned Transformer for Electrocardiogram Classification

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BaT: Beat-aligned Transformer for Electrocardiogram Classifi...

IEEE International Conference on data Mining (ICDM)

作者： Xiaoyu Li Chen Li Yuhua Wei Yuyao Sun Jishang Wei Xiang Li Buyue Qian School of Computer Science and Technology Xi’an Jiaotong University Xi’an Shaanxi China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an Shaanxi China Ping An Healthcare Technology Beijing China HP Labs 1501 Page Mill Rd Palo Alto CA USA The First Affiliated Hospital of Xi’an Jiaotong University Xi’an Shaanxi China

ISBN: (纸本)9781665423991

Electrocardiogram (ECG) is one of the critical diagnostic tools in healthcare. Various deep learning models, except Transformers, have been explored and applied to map ECG patterns to heart abnormalities. Transformer models have been adopted from natural language processing to computer vision with advanced features. Most recently, vision transformers show exceptional performances, even on moderate-scale datasets. However, naively applying vision transformers on electrocardiogram datasets leads to poor results. In this paper, we propose a novel network called Beat-aligned Transformer (BaT), a hierarchical Transformer that sufficiently exploits the cyclicity of ECG. We organize and treat an input ECG as multiple aligned beats instead of a single time series. In the BaT, shifted-window-based Transformer blocks (SW Block) are adopted to learn the representation for each beat, and aggregation blocks are designed to exchange information among the beat representations. Nested SW Blocks and aggregation blocks form a beat-aware hierarchical structure of BaT. In this way, the new data format and the BaT hierarchical structure boost Transformer performance on ECG classification. From the experiments on public ECG datasets, we observe BaT outperforms other Transformer-based models and achieves competitive performance compared with other state-of-the-art methods.

关键词： Heart Deep learning Computational modeling Conferences Time series analysis Medical services Electrocardiography