scholarly journals A Review of Explainable Deep Learning Cancer Detection Models in Medical Imaging

2021 ◽  
Vol 11 (10) ◽  
pp. 4573
Author(s):  
Mehmet A. Gulum ◽  
Christopher M. Trombley ◽  
Mehmed Kantardzic
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Cancer Detection ◽  
Black Box ◽  
Significant Problem ◽  
Clinical Settings ◽  
Clinical Implementation ◽  
Mr Images ◽  
Clinical Tool ◽  
Learning Architectures

Deep learning has demonstrated remarkable accuracy analyzing images for cancer detection tasks in recent years. The accuracy that has been achieved rivals radiologists and is suitable for implementation as a clinical tool. However, a significant problem is that these models are black-box algorithms therefore they are intrinsically unexplainable. This creates a barrier for clinical implementation due to lack of trust and transparency that is a characteristic of black box algorithms. Additionally, recent regulations prevent the implementation of unexplainable models in clinical settings which further demonstrates a need for explainability. To mitigate these concerns, there have been recent studies that attempt to overcome these issues by modifying deep learning architectures or providing after-the-fact explanations. A review of the deep learning explanation literature focused on cancer detection using MR images is presented here. The gap between what clinicians deem explainable and what current methods provide is discussed and future suggestions to close this gap are provided.

scholarly journals Comparison of different deep learning architectures for synthetic CT generation from MR images

2021 ◽  
Vol 90 ◽  
pp. 99-107
Author(s):  
Abbas Bahrami ◽  
Alireza Karimian ◽  
Hossein Arabi
Keyword(s):  
Deep Learning ◽  
Mr Images ◽  
Learning Architectures ◽  
Synthetic Ct

scholarly journals The Reproducibility of Deep Learning-Based Segmentation of the Prostate Gland and Zones on T2-Weighted MR Images

Diagnostics ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1690
Author(s):  
Mohammed R. S. Sunoqrot ◽  
Kirsten M. Selnæs ◽  
Elise Sandsmark ◽  
Sverre Langørgen ◽  
Helena Bertilsson ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prostate Gland ◽  
Peripheral Zone ◽  
Clinical Implementation ◽  
Mr Images ◽  
Cad Systems ◽  
Prostate Segmentation ◽  
Volume Of Interest ◽  
Segmentation Methods ◽  
Detection And Diagnosis

Volume of interest segmentation is an essential step in computer-aided detection and diagnosis (CAD) systems. Deep learning (DL)-based methods provide good performance for prostate segmentation, but little is known about the reproducibility of these methods. In this work, an in-house collected dataset from 244 patients was used to investigate the intra-patient reproducibility of 14 shape features for DL-based segmentation methods of the whole prostate gland (WP), peripheral zone (PZ), and the remaining prostate zones (non-PZ) on T2-weighted (T2W) magnetic resonance (MR) images compared to manual segmentations. The DL-based segmentation was performed using three different convolutional neural networks (CNNs): V-Net, nnU-Net-2D, and nnU-Net-3D. The two-way random, single score intra-class correlation coefficient (ICC) was used to measure the inter-scan reproducibility of each feature for each CNN and the manual segmentation. We found that the reproducibility of the investigated methods is comparable to manual for all CNNs (14/14 features), except for V-Net in PZ (7/14 features). The ICC score for segmentation volume was found to be 0.888, 0.607, 0.819, and 0.903 in PZ; 0.988, 0.967, 0.986, and 0.983 in non-PZ; 0.982, 0.975, 0.973, and 0.984 in WP for manual, V-Net, nnU-Net-2D, and nnU-Net-3D, respectively. The results of this work show the feasibility of embedding DL-based segmentation in CAD systems, based on multiple T2W MR scans of the prostate, which is an important step towards the clinical implementation.

scholarly journals Methods for Host-Based Intrusion Detection with Deep Learning

2021 ◽  
Author(s):  
John Ring ◽  
Colin Van Oort ◽  
Samson Durst ◽  
Vanessa White ◽  
Joseph Near ◽  
...  
Keyword(s):  
Deep Learning ◽  
Intrusion Detection ◽  
Black Box ◽  
Intrusion Detection Systems ◽  
System Behavior ◽  
Detection Systems ◽  
System Calls ◽  
Baseline System ◽  
Sequence Behavior ◽  
Learning Architectures

Host-based Intrusion Detection Systems (HIDS) automatically detect events that indicate compromise by adversarial applications. HIDS are generally formulated as analyses of sequences of system events such as bash commands or system calls. Anomaly-based approaches to HIDS leverage models of normal (aka baseline) system behavior to detect and report abnormal events, and have the advantage of being able to detect novel attacks. In this paper we develop a new method for anomaly-based HIDS using deep learning predictions of sequence-to-sequence behavior in system calls. Our proposed method, called the ALAD algorithm, aggregates predictions at the application level to detect anomalies. We investigate the use of several deep learning architectures, including WaveNet and several recurrent networks. We show that ALAD empowered with deep learning significantly outperforms previous approaches. We train and evaluate our models using an existing dataset, ADFA-LD, and a new dataset of our own construction, PLAID. As deep learning models are black box in nature we use an alternate approach, allotaxonographs, to characterize and understand differences in baseline vs.~attack sequences in HIDS datasets such as PLAID.

scholarly journals A Review of the Evolution of Deep Learning Architectures and Comparison of their Performances for Histopathologic Cancer Detection

2020 ◽  
Vol 46 ◽  
pp. 683-689
Author(s):  
Jingpeng Zhai ◽  
Weiran Shen ◽  
Ishwar Singh ◽  
Tom Wanyama ◽  
Zhen Gao
Keyword(s):  
Deep Learning ◽  
Cancer Detection ◽  
Learning Architectures

scholarly journals Standardization of imaging methods for machine learning in neuro-oncology

2020 ◽  
Vol 2 (Supplement_4) ◽  
pp. iv49-iv55
Author(s):  
Xiao Tian Li ◽  
Raymond Y Huang
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Medical Imaging ◽  
Clinical Settings ◽  
Collaborative Effort ◽  
Imaging Data ◽  
Clinical Implementation ◽  
Novel Technique ◽  
Oncological Imaging ◽  
Medical Imaging Data

Abstract Radiomics is a novel technique in which quantitative phenotypes or features are extracted from medical images. Machine learning enables analysis of large quantities of medical imaging data generated by radiomic feature extraction. A growing number of studies based on these methods have developed tools for neuro-oncology applications. Despite the initial promises, many of these imaging tools remain far from clinical implementation. One major limitation hindering the use of these models is their lack of reproducibility when applied across different institutions and clinical settings. In this article, we discuss the importance of standardization of methodology and reporting in our effort to improve reproducibility. Ongoing efforts of standardization for neuro-oncological imaging are reviewed. Challenges related to standardization and potential disadvantages in over-standardization are also described. Ultimately, greater multi-institutional collaborative effort is needed to provide and implement standards for data acquisition and analysis methods to facilitate research results to be interoperable and reliable for integration into different practice environments.

scholarly journals Brain Tumor IDH, 1p/19q, and MGMT Molecular Classification Using MRI-based Deep Learning: Effect of Motion and Motion Correction

2020 ◽  
Author(s):  
Sahil S. Nalawade ◽  
Fang F. Yu ◽  
Chandan Ganesh Bangalore Yogananda ◽  
Gowtham K. Murugesan ◽  
Bhavya R. Shah ◽  
...  
Keyword(s):  
Deep Learning ◽  
Motion Correction ◽  
Molecular Classification ◽  
Motion Artifact ◽  
Learning Networks ◽  
Clinical Implementation ◽  
Mr Images ◽  
Patient Motion ◽  
Classifier Performance ◽  
Molecular Profiles

AbstractDeep learning has shown promise for predicting glioma molecular profiles using MR images. Before clinical implementation, ensuring robustness to real-world problems, such as patient motion, is crucial. We sought to evaluate the effects of motion artifact on glioma marker classifier performance and develop a deep learning motion correction network to restore classification accuracies. T2w images and molecular information were retrieved from the TCIA and TCGA databases. Three-fold cross-validation was used to train and test the motion correction network on artifact-corrupted images. We then compared the performance of three glioma marker classifiers (IDH mutation, 1p/19q codeletion, and MGMT methylation) using motion-corrupted and motion-corrected images. Glioma marker classifier performance decreased markedly with increasing motion corruption. Applying motion correction effectively restored classification accuracy for even the most motion-corrupted images. For IDH classification, an accuracy of 99% was achieved, representing a new benchmark in non-invasive image-based IDH classification and exceeding the original performance of the network. Robust motion correction can enable high accuracy in deep learning MRI-based molecular marker classification rivaling tissue-based characterization.STATEMENT OF SIGNIFICANCEDeep learning networks have shown promise for predicting molecular profiles of gliomas using MR images. We demonstrate that patient motion artifact, which is frequently encountered in the clinic, can significantly impair the performance of these algorithms. The application of robust motion correction algorithms can restore the performance of these networks, rivaling tissue-based characterization.

Review of the Applications of Deep Learning in Bioinformatics

2021 ◽  
Vol 15 (8) ◽  
pp. 898-911
Author(s):  
Yongqing Zhang ◽  
Jianrong Yan ◽  
Siyu Chen ◽  
Meiqin Gong ◽  
Dongrui Gao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Discovery ◽  
Biomedical Imaging ◽  
State Of The Art ◽  
Black Box ◽  
Medical Data ◽  
Biological Data ◽  
High Dimensional ◽  
Biological Research ◽  
Process Data

Rapid advances in biological research over recent years have significantly enriched biological and medical data resources. Deep learning-based techniques have been successfully utilized to process data in this field, and they have exhibited state-of-the-art performances even on high-dimensional, nonstructural, and black-box biological data. The aim of the current study is to provide an overview of the deep learning-based techniques used in biology and medicine and their state-of-the-art applications. In particular, we introduce the fundamentals of deep learning and then review the success of applying such methods to bioinformatics, biomedical imaging, biomedicine, and drug discovery. We also discuss the challenges and limitations of this field, and outline possible directions for further research.

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