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.

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 Pathologists' Computer-Assisted Diagnosis: A Mock-up of a Prototype Information System to Facilitate Automation of Pathology Sign-out

2017 ◽  
Vol 141 (10) ◽  
pp. 1413-1420 ◽  
Author(s):  
Navid Farahani ◽  
Zheng Liu ◽  
Dylan Jutt ◽  
Jeffrey L. Fine
Keyword(s):  
Breast Cancer ◽  
Computer Assisted ◽  
Pathology Report ◽  
Heterogeneous Information ◽  
Computer Assisted Diagnosis ◽  
Glass Slides ◽  
Cancer Specimen ◽  
Assisted Diagnosis ◽  
Deep Integration ◽  
Whole Slide Images

Context.— Pathologists' computer-assisted diagnosis (pCAD) is a proposed framework for alleviating challenges through the automation of their routine sign-out work. Currently, hypothetical pCAD is based on a triad of advanced image analysis, deep integration with heterogeneous information systems, and a concrete understanding of traditional pathology workflow. Prototyping is an established method for designing complex new computer systems such as pCAD. Objective.— To describe, in detail, a prototype of pCAD for the sign-out of a breast cancer specimen. Design.— Deidentified glass slides and data from breast cancer specimens were used. Slides were digitized into whole-slide images with an Aperio ScanScope XT, and screen captures were created by using vendor-provided software. The advanced workflow prototype was constructed by using PowerPoint software. Results.— We modeled an interactive, computer-assisted workflow: pCAD previews whole-slide images in the context of integrated, disparate data and predefined diagnostic tasks and subtasks. Relevant regions of interest (ROIs) would be automatically identified and triaged by the computer. A pathologist's sign-out work would consist of an interactive review of important ROIs, driven by required diagnostic tasks. The interactive session would generate a pathology report automatically. Conclusions.— Using animations and real ROIs, the pCAD prototype demonstrates the hypothetical sign-out in a stepwise fashion, illustrating various interactions and explaining how steps can be automated. The file is publicly available and should be widely compatible. This mock-up is intended to spur discussion and to help usher in the next era of digitization for pathologists by providing desperately needed and long-awaited automation.

scholarly journals Aggregation-and-Attention Network for brain tumor segmentation

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Chih-Wei Lin ◽  
Yu Hong ◽  
Jinfu Liu
Keyword(s):  
Brain Tumor ◽  
Semantic Information ◽  
Spatial Relationship ◽  
Tumor Segmentation ◽  
Computer Assisted ◽  
Brain Tumor Segmentation ◽  
Attention Network ◽  
Multi Scale ◽  
Assisted Diagnosis ◽  
The Brain

Abstract Background Glioma is a malignant brain tumor; its location is complex and is difficult to remove surgically. To diagnosis the brain tumor, doctors can precisely diagnose and localize the disease using medical images. However, the computer-assisted diagnosis for the brain tumor diagnosis is still the problem because the rough segmentation of the brain tumor makes the internal grade of the tumor incorrect. Methods In this paper, we proposed an Aggregation-and-Attention Network for brain tumor segmentation. The proposed network takes the U-Net as the backbone, aggregates multi-scale semantic information, and focuses on crucial information to perform brain tumor segmentation. To this end, we proposed an enhanced down-sampling module and Up-Sampling Layer to compensate for the information loss. The multi-scale connection module is to construct the multi-receptive semantic fusion between encoder and decoder. Furthermore, we designed a dual-attention fusion module that can extract and enhance the spatial relationship of magnetic resonance imaging and applied the strategy of deep supervision in different parts of the proposed network. Results Experimental results show that the performance of the proposed framework is the best on the BraTS2020 dataset, compared with the-state-of-art networks. The performance of the proposed framework surpasses all the comparison networks, and its average accuracies of the four indexes are 0.860, 0.885, 0.932, and 1.2325, respectively. Conclusions The framework and modules of the proposed framework are scientific and practical, which can extract and aggregate useful semantic information and enhance the ability of glioma segmentation.

scholarly journals Deep Transfer Learning for Improved Detection of Keratoconus using Corneal Topographic Maps

2021 ◽  
Author(s):  
Ali H. Al-Timemy ◽  
Nebras H. Ghaeb ◽  
Zahraa M. Mosa ◽  
Javier Escudero
Keyword(s):  
Transfer Learning ◽  
Learning Strategy ◽  
Clinical Decision ◽  
Topographic Maps ◽  
Computer Assisted ◽  
Clinical Knowledge ◽  
Average Accuracy ◽  
Assisted Diagnosis ◽  
Improved Accuracy ◽  
Fine Tune

Abstract Clinical keratoconus (KCN) detection is a challenging and time-consuming task. In the diagnosis process, ophthalmologists must revise demographic and clinical ophthalmic examinations. The latter include slit-lamb, corneal topographic maps, and Pentacam indices (PI). We propose an Ensemble of Deep Transfer Learning (EDTL) based on corneal topographic maps. We consider four pretrained networks, SqueezeNet (SqN), AlexNet (AN), ShuffleNet (SfN), and MobileNet-v2 (MN), and fine-tune them on a dataset of KCN and normal cases, each including four topographic maps. We also consider a PI classifier. Then, our EDTL method combines the output probabilities of each of the five classifiers to obtain a decision based on the fusion of probabilities. Individually, the classifier based on PI achieved 93.1% accuracy, whereas the deep classifiers reached classification accuracies over 90% only in isolated cases. Overall, the average accuracy of the deep networks over the four corneal maps ranged from 86% (SfN) to 89.9% (AN). The classifier ensemble increased the accuracy of the deep classifiers based on corneal maps to values ranging (92.2% to 93.1%) for SqN and (93.1% to 94.8%) for AN. Including in the ensemble-specific combinations of corneal maps’ classifiers and PI increased the accuracy to 98.3%. Moreover, visualization of first learner filters in the networks and Grad-CAMs confirmed that the networks had learned relevant clinical features. This study shows the potential of creating ensembles of deep classifiers fine-tuned with a transfer learning strategy as it resulted in an improved accuracy while showing learnable filters and Grad-CAMs that agree with clinical knowledge. This is a step further towards the potential clinical deployment of an improved computer-assisted diagnosis system for KCN detection to help ophthalmologists to confirm the clinical decision and to perform fast and accurate KCN treatment.

scholarly journals Computer-Assisted Diagnosis System for Breast Cancer in Computed Tomography Laser Mammography (CTLM)

2017 ◽  
Vol 30 (6) ◽  
pp. 796-811 ◽  
Author(s):  
Afsaneh Jalalian ◽  
Syamsiah Mashohor ◽  
Rozi Mahmud ◽  
Babak Karasfi ◽  
M. Iqbal Saripan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Computed Tomography ◽  
Computer Assisted ◽  
Diagnosis System ◽  
Computer Assisted Diagnosis ◽  
Assisted Diagnosis

Specialty-Training Program During COVID-19 Pandemic: A Single Center Survey on over 300 Trainees and Trainers (Preprint)

2021 ◽  
Author(s):  
Ipek Kivilcim Oguzulgen ◽  
Ayşe Kalkancı ◽  
Mehmet Suhan Ayhan ◽  
Ulver Derici ◽  
Alpaslan Senkoylu ◽  
...  
Keyword(s):  
Training Program ◽  
University Hospital ◽  
Computer Assisted ◽  
Multiple Choice Questions ◽  
Online Questionnaire ◽  
Elective Surgical Procedures ◽  
Institutional Review ◽  
Speciality Training ◽  
Learning Platforms ◽  
New Perspective

BACKGROUND COVID-19 pandemic caused significant modifications such as limiting the number of residents in the clinics, cancelling elective surgical procedures, stopping face to face practical education, and transforming theoretical education into distance learning platforms resulted in alterations in the curriculum. OBJECTIVE We addressed to assess the situation of trainees’ education using an online questionnaire from the trainees’ and directors’ perspective during the pandemic. METHODS The survey platform SurveyMonkey® was used to distribute the survey and to collect responses. We generated a list of multiple-choice questions about how social distancing affected the delivery of medical education, potential compromise in core training and difficulties in conducting clinical research for the thesis. RESULTS A total of 364 trainees among 552 (65.9%) under training at our university hospital and 90% of the directors (37 of 41) responded the survey. Almost 78 percent of the trainees claimed that they have been negatively affected during the pandemic. Although majority of the trainees (60,3%) reported that extension of their education program is not necessary, most of the program directors were in tendency of extending the duration of the speciality training period. The participants predominantly considered that online training would keep on being a part of the training program after the pandemic. CONCLUSIONS Education programs are negatively affected during pandemics. However, authorities should manage this deficiency by a new perspective since present trainees are familiar to use technology-driven virtual sources for their education. After the pandemic, computer-assisted online learning and web-based programs should be integrated into educational curriculum. CLINICALTRIAL The study was approved from the institutional review board of Gazi University Ethics Committee (Approval Number: 2021-276).

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