scholarly journals Tensor Decomposition for Multiple-Instance Classification of High-Order Medical Data

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Thomas Papastergiou ◽  
Evangelia I. Zacharaki ◽  
Vasileios Megalooikonomou
Keyword(s):  
Health Care ◽  
Missing Values ◽  
Tensor Decomposition ◽  
Multiple Instance Learning ◽  
High Order ◽  
Clinical Diagnostics ◽  
Multidimensional Data ◽  
Computer Assisted ◽  
Assisted Diagnosis

Multidimensional data that occur in a variety of applications in clinical diagnostics and health care can naturally be represented by multidimensional arrays (i.e., tensors). Tensor decompositions offer valuable and powerful tools for latent concept discovery that can handle effectively missing values and noise. We propose a seamless, application-independent feature extraction and multiple-instance (MI) classification method, which represents the raw multidimensional, possibly incomplete, data by means of learning a high-order dictionary. The effectiveness of the proposed method is demonstrated in two application scenarios: (i) prediction of frailty in older people using multisensor recordings and (ii) breast cancer classification based on histopathology images. The proposed method outperforms or is comparable to the state-of-the-art multiple-instance learning classifiers highlighting its potential for computer-assisted diagnosis and health care support.

scholarly journals Detection and classification of contrast-enhancing masses by a fully automatic computer-assisted diagnosis system for breast MRI

2012 ◽  
Vol 35 (5) ◽  
pp. 1077-1088 ◽  
Author(s):  
Diane M. Renz ◽  
Joachim Böttcher ◽  
Felix Diekmann ◽  
Alexander Poellinger ◽  
Martin H. Maurer ◽  
...  
Keyword(s):  
Breast Mri ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Automatic Computer ◽  
Computer Assisted Diagnosis ◽  
Fully Automatic ◽  
Assisted Diagnosis

Dental Radiograph Segmentation and Classification—A Comparative Study of Hu’s Moments and Histogram of Oriented Gradients

2019 ◽  
Vol 16 (8) ◽  
pp. 3612-3616
Author(s):  
Rameswari Poornima Janardanan ◽  
Rajasvaran Logeswaran
Keyword(s):  
Support Vector ◽  
Computer Assisted ◽  
X Rays ◽  
Histogram Of Oriented Gradients ◽  
Moment Invariant ◽  
Feature Descriptors ◽  
Assisted Diagnosis ◽  
Classification Of Images ◽  
Comparison Of The Results

This paper proposes a method to compare two feature descriptors to classify dental X-rays, using Hu’s Moments (HM) and the Histogram of Oriented Gradients (HOG). The dental radiographs are preprocessed, and the shape features of teeth are derived using HM and HOG. Support Vector Machine (SVM) is then used for tooth classification and recognition. Comparison of the results of using the two approaches as feature descriptors revealed that regardless of its orientation, size and position, moment invariant functions are very useful for object classification. The classification of images into molar and premolar has been done on manually cropped images. This method was validated on periapical radiographs. Results obtained show that using both HM and HOG to classify and recognize teeth shape description accuracy as better than, or at least comparable, to the state-of-the-art approaches. This work aids to improve the computer-assisted diagnosis and decision in dentistry. The forensic odonatological applications of this approach are wide and of immense benefits in both forensic and biometric identification.

scholarly journals Palpable Breast Lump Triage by Minimally Trained Operators in Mexico Using Computer-Assisted Diagnosis and Low-Cost Ultrasound

2018 ◽  
pp. 1-9 ◽  
Author(s):  
Susan M. Love ◽  
Wendie A. Berg ◽  
Christine Podilchuk ◽  
Ana Lilia López Aldrete ◽  
Aarón Patricio Gaxiola Mascareño ◽  
...  
Keyword(s):  
Health Care ◽  
Health Care Workers ◽  
Low Cost ◽  
Computer Assisted ◽  
Portable Ultrasound ◽  
Care Workers ◽  
Ultrasound System ◽  
Palpable Breast ◽  
Assisted Diagnosis ◽  
Breast Lumps

Purpose In low- to middle-income countries (LMICs), most breast cancers present as palpable lumps; however, most palpable lumps are benign. We have developed artificial intelligence–based computer-assisted diagnosis (CADx) for an existing low-cost portable ultrasound system to triage which lumps need further evaluation and which are clearly benign. This pilot study was conducted to demonstrate that this approach can be successfully used by minimally trained health care workers in an LMIC country. Patients and Methods We recruited and trained three nonradiologist health care workers to participate in an institutional review board–approved, Health Insurance Portability and Accountability Act–compliant pilot study in Jalisco, Mexico, to determine whether they could use portable ultrasound (GE Vscan Dual Probe) to acquire images of palpable breast lumps of adequate quality for accurate computer analysis. Images from 32 women with 32 breast masses were then analyzed with a triage-CADx system, generating an output of benign or suspicious (biopsy recommended). Triage-CADx outputs were compared with radiologist readings. Results The nonradiologists were able to acquire adequate images. Triage by the CADx software was as accurate as assessment by specialist radiologists, with two (100%) of two cancers considered suspicious and 30 (100%) of 30 benign lesions classified as benign. Conclusion A portable ultrasound system with CADx software can be successfully used by first-level health care workers to triage palpable breast lumps. These results open up the possibility of implementing practical, cost-effective triage of palpable breast lumps, ensuring that scarce resources can be dedicated to suspicious lesions requiring further workup.

A computer assisted diagnosis tool for the classification of burns by depth of injury

Burns ◽  
2005 ◽  
Vol 31 (3) ◽  
pp. 275-281 ◽  
Author(s):  
Carmen Serrano ◽  
Begoña Acha ◽  
Tomás Gómez-Cía ◽  
José I. Acha ◽  
Laura M. Roa
Keyword(s):  
Computer Assisted ◽  
Computer Assisted Diagnosis ◽  
Diagnosis Tool ◽  
Assisted Diagnosis

scholarly journals The value of GATA6 immunohistochemistry and computer-assisted diagnosis to predict clinical outcome in advanced pancreatic cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kai Duan ◽  
Gun-Ho Jang ◽  
Robert C. Grant ◽  
Julie M. Wilson ◽  
Faiyaz Notta ◽  
...  
Keyword(s):  
Clinical Outcome ◽  
Open Source Software ◽  
Digital Pathology ◽  
Advanced Pancreatic Cancer ◽  
Molecular Data ◽  
Ductal Adenocarcinoma ◽  
Computer Assisted ◽  
Biopsy Specimens ◽  
Assisted Diagnosis

AbstractCombination chemotherapy, either modified FOLFIRINOX (mFFX) or gemcitabine–nabpaclitaxel, are used in the treatment of most patients with advanced pancreatic ductal adenocarcinoma (PDAC), yet robust biomarkers of outcome are currently lacking to guide regimen selection. Here, we tested GATA6 immunohistochemistry (IHC) as a putative biomarker in advanced PDAC. GATA6 is a transcription factor in normal pancreas development. Two pathologists, blinded to clinical and molecular data, independently assessed GATA6 IHC in biopsy specimens of 130 patients with advanced PDAC, in 2 distinct phases (without and with computer assistance using the open source software QuPath). Low GATA6 IHC expression was associated with shorter overall survival [median OS 6.2 months for patients with GATA6 low tumors vs. 11.5 months for patients with GATA6 high tumors, HR 1.66 (95% CI 1.15–2.40), P = 0.007]. Progression appears to be higher in GATA6-low tumors compared to GATA6-high tumors in patients treated with mFFX (P = 0.024) but not in patients treated with gemcitabine regimens. GATA6 IHC expression was significantly associated with molecular subtypes (P = 0.0003). Digital assistance markedly improved interrater concordance (Cohen’s kappa scores of 0.32 vs. 0.95). Our results provide strong evidence that GATA6 IHC can be used as a single biomarker in the clinic to predict clinical outcome in advanced PDAC, warranting further investigation in prospective clinical trials. These results provide the basis for an improved classification of PDAC and future biomarker design using digital pathology workflow.

scholarly journals A 3D multiscale view convolutional neural network with attention for mental disease diagnosis on MRI images

2021 ◽  
Vol 18 (5) ◽  
pp. 6978-3994
Author(s):  
Zijian Wang ◽  
◽  
Yaqin Zhu ◽  
Haibo Shi ◽  
Yanting Zhang ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Mental Disease ◽  
Computer Assisted ◽  
Multi Scale ◽  
Mental Diseases ◽  
Assisted Diagnosis ◽  
3D Cnn

<abstract> <p>Computer Assisted Diagnosis (CAD) based on brain Magnetic Resonance Imaging (MRI) is a popular research field for the computer science and medical engineering. Traditional machine learning and deep learning methods were employed in the classification of brain MRI images in the previous studies. However, the current algorithms rarely take into consideration the influence of multi-scale brain connectivity disorders on some mental diseases. To improve this defect, a deep learning structure was proposed based on MRI images, which was designed to consider the brain's connections at different sizes and the attention of connections. In this work, a Multiscale View (MV) module was proposed, which was designed to detect multi-scale brain network disorders. On the basis of the MV module, the path attention module was also proposed to simulate the attention selection of the parallel paths in the MV module. Based on the two modules, we proposed a 3D Multiscale View Convolutional Neural Network with Attention (3D MVA-CNN) for classification of MRI images for mental disease. The proposed method outperformed the previous 3D CNN structures in the structural MRI data of ADHD-200 and the functional MRI data of schizophrenia. Finally, we also proposed a preliminary framework for clinical application using 3D CNN, and discussed its limitations on data accessing and reliability. This work promoted the assisted diagnosis of mental diseases based on deep learning and provided a novel 3D CNN method based on MRI data.</p> </abstract>

scholarly journals NECScanNet: Novel Method for Cervical Neuroendocrine Cancer Screening from Whole Slide Images

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xin Liao ◽  
Qin Huang ◽  
Xin Zheng
Keyword(s):  
Recall Rate ◽  
Multiple Instance Learning ◽  
Automatic Recognition ◽  
Pathological Diagnosis ◽  
University Hospital ◽  
Computer Assisted ◽  
Neuroendocrine Cancer ◽  
Assisted Diagnosis ◽  
Deep Convolution Network ◽  
Whole Slide Images

As a rare malignant tumor, cervical neuroendocrine cancer (NEC) is difficult in diagnosis even for experienced pathologists. A computer-assisted diagnosis may be helpful for the improvement of diagnostic accuracy. Nevertheless, the computer-aided pathological diagnosis has to face a great challenge that the hundred-million-pixels or even gig-pixels whole slide images (WSIs) cannot be applied directly in the existing deep convolution network for training and analysis. Therefore, the construction of a neural network to realize the automatic screening of cervical NEC is challenging; meanwhile, as far as we know, little attention has been paid to this field. In order to address this problem, here we present a multiple-instance learning method for automatic recognition of cervical NEC on pathological WSI, which consists of the Sliding Detector module and Lesion Analyzer module. A pathological WSI dataset, which is composed of 84 NEC cases and 216 NEC-free cases from the Pathological Department of West China Second University Hospital, is applied to evaluate the performance of the method. The experimental results show that the recall rate, accuracy rate, and precision rate of our method for automatic recognition are 92.9%, 92.7%, and 83.0%, respectively, demonstrating the effectiveness and the potential in clinical practice. The application of this method in computer-assisted pathological diagnosis is expected to decrease the misdiagnosis as well as the false diagnosis of rare cervical NEC, and, consequently, improve the therapeutic effect of cervical cancers.

Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

2014 ◽  
Vol 18 (5) ◽  
pp. 792-797 ◽  
Author(s):  
Samantha Denise F. Hilado ◽  
◽  
Laurence A. Gan Lim ◽  
Raouf N. G. Naguib ◽  
Elmer P. Dadios ◽  
...  
Keyword(s):  
Neural Networks ◽  
Colon Cancer ◽  
Death Rate ◽  
Computer Assisted ◽  
Histopathological Classification ◽  
High Death Rate ◽  
High Death ◽  
Histopathological Images ◽  
Assisted Diagnosis

Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of accuracy.

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