scholarly journals Training calibration-based counterfactual explainers for deep learning models in medical image analysis

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Kowshik Thopalli ◽  
Deepta Rajan ◽  
Pavan Turaga
Keyword(s):  
Medical Image ◽  
State Of The Art ◽  
Empirical Studies ◽  
Medical Image Analysis ◽  
X Ray ◽  
Irrelevant Feature ◽  
Holistic Understanding ◽  
Model Predictions ◽  
Chest X Ray ◽  
Decision Boundaries

AbstractThe rapid adoption of artificial intelligence methods in healthcare is coupled with the critical need for techniques to rigorously introspect models and thereby ensure that they behave reliably. This has led to the design of explainable AI techniques that uncover the relationships between discernible data signatures and model predictions. In this context, counterfactual explanations that synthesize small, interpretable changes to a given query while producing desired changes in model predictions have become popular. This under-constrained, inverse problem is vulnerable to introducing irrelevant feature manipulations, particularly when the model’s predictions are not well-calibrated. Hence, in this paper, we propose the TraCE (training calibration-based explainers) technique, which utilizes a novel uncertainty-based interval calibration strategy for reliably synthesizing counterfactuals. Given the wide-spread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, to detect shortcuts, and to infer relationships between patient attributes and disease severity.

Training Calibration-based Counterfactual Explainers for Deep Learning Models in Medical Image Analysis

2021 ◽  
Author(s):  
Jayaraman J. Thiagarajan ◽  
Kowshik Thopalli ◽  
Deepta Rajan ◽  
Pavan Turaga
Keyword(s):  
Image Analysis ◽  
Disease Severity ◽  
Medical Image ◽  
Empirical Studies ◽  
Medical Image Analysis ◽  
Generative Models ◽  
Uncertainty Estimates ◽  
Holistic Understanding ◽  
Chest X Ray ◽  
Decision Boundaries

Abstract Artificial intelligence methods such as deep neural networks promise unprecedented capabilities in healthcare, from diagnosing diseases to prescribing treatments. While this can eventually produce a valuable suite of tools for automating clinical workflows, a critical step forward is to ensure that the predictive models are reliable and to enable a rigorous introspection of their behavior. This has led to the design of explainable AI techniques that are aimed at uncovering the relationships between discernible data signatures and decisions from machine-learned models, and characterizing strengths/weaknesses of models. In this context, the so-called counterfactual explanations that synthesize small, interpretable changes to a given query sample while producing desired changes in model predictions to support user-specified hypotheses (e.g., progressive change in disease severity) have become popular. When a model’s predictions are not well-calibrated (i.e., the prediction confidences are not indicative of the likelihood of the predictions being correct), the inverse problem of synthesizing counterfactuals can produce explanations with irrelevant feature manipulations. Hence, in this paper, we propose to leverage prediction uncertainties from the learned models to better guide this optimization. To this end, we present TraCE (Training Calibration-based Explainers), a counterfactual generation approach for deep models in medical image analysis, which utilizes pre-trained generative models and a novel uncertainty-based interval calibration strategy for synthesizing hypothesis-driven explanations. By leveraging uncertainty estimates in the optimization process, TraCE can consistently produce meaningful counterfactual evidences and elucidate complex decision boundaries learned by deep classifiers. Furthermore, we demonstrate the effectiveness of TraCE in revealing intricate relationships between different patient attributes and in detecting shortcuts, arising from unintended biases, in learned models. Given the widespread adoption of machine-learned solutions in radiology, our study focuses on deep models used for identifying anomalies in chest X-ray images. Using rigorous empirical studies, we demonstrate the superiority of TraCE explanations over several state-of-the-art baseline approaches, in terms of several widely adopted evaluation metrics in counterfactual reasoning. Our findings show that TraCE can be used to obtain a holistic understanding of deep models by enabling progressive exploration of decision boundaries, detecting shortcuts, and inferring relationships between patient attributes and disease severity.

scholarly journals COVID-19 infection map generation and detection from chest X-ray images

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Aysen Degerli ◽  
Mete Ahishali ◽  
Mehmet Yamac ◽  
Serkan Kiranyaz ◽  
Muhammad E. H. Chowdhury ◽  
...  
Keyword(s):  
State Of The Art ◽  
Ground Truth ◽  
Clinical Use ◽  
X Ray ◽  
Learning Techniques ◽  
Map Generation ◽  
Severity Grading ◽  
Chest X Ray ◽  
Novel Method ◽  
Aided Diagnosis

AbstractComputer-aided diagnosis has become a necessity for accurate and immediate coronavirus disease 2019 (COVID-19) detection to aid treatment and prevent the spread of the virus. Numerous studies have proposed to use Deep Learning techniques for COVID-19 diagnosis. However, they have used very limited chest X-ray (CXR) image repositories for evaluation with a small number, a few hundreds, of COVID-19 samples. Moreover, these methods can neither localize nor grade the severity of COVID-19 infection. For this purpose, recent studies proposed to explore the activation maps of deep networks. However, they remain inaccurate for localizing the actual infestation making them unreliable for clinical use. This study proposes a novel method for the joint localization, severity grading, and detection of COVID-19 from CXR images by generating the so-called infection maps. To accomplish this, we have compiled the largest dataset with 119,316 CXR images including 2951 COVID-19 samples, where the annotation of the ground-truth segmentation masks is performed on CXRs by a novel collaborative human–machine approach. Furthermore, we publicly release the first CXR dataset with the ground-truth segmentation masks of the COVID-19 infected regions. A detailed set of experiments show that state-of-the-art segmentation networks can learn to localize COVID-19 infection with an F1-score of 83.20%, which is significantly superior to the activation maps created by the previous methods. Finally, the proposed approach achieved a COVID-19 detection performance with 94.96% sensitivity and 99.88% specificity.

scholarly journals Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

2021 ◽  
Vol 7 (2) ◽  
pp. 19
Author(s):  
Tirivangani Magadza ◽  
Serestina Viriri
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Brain Tumor ◽  
Quantitative Analysis ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
Building Blocks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

Quantitative analysis of the brain tumors provides valuable information for understanding the tumor characteristics and treatment planning better. The accurate segmentation of lesions requires more than one image modalities with varying contrasts. As a result, manual segmentation, which is arguably the most accurate segmentation method, would be impractical for more extensive studies. Deep learning has recently emerged as a solution for quantitative analysis due to its record-shattering performance. However, medical image analysis has its unique challenges. This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies. We end with a critical discussion of open challenges in medical image analysis.

scholarly journals COVID-19 Diagnosis Using Capsule Network and Fuzzy C -Means and Mayfly Optimization Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Ali Farki ◽  
Zahra Salekshahrezaee ◽  
Arash Mohammadi Tofigh ◽  
Reza Ghanavati ◽  
Behdad Arandian ◽  
...  
Keyword(s):  
State Of The Art ◽  
Optimal Method ◽  
X Ray ◽  
Suggested Technique ◽  
Improve Accuracy ◽  
Sensitivity Rate ◽  
Chest X Ray ◽  
Therapeutic Measures ◽  
Simulation Results ◽  
Day By Day

The COVID-19 epidemic is spreading day by day. Early diagnosis of this disease is essential to provide effective preventive and therapeutic measures. This process can be used by a computer-aided methodology to improve accuracy. In this study, a new and optimal method has been utilized for the diagnosis of COVID-19. Here, a method based on fuzzy C -ordered means (FCOM) along with an improved version of the enhanced capsule network (ECN) has been proposed for this purpose. The proposed ECN method is improved based on mayfly optimization (MFO) algorithm. The suggested technique is then implemented on the chest X-ray COVID-19 images from publicly available datasets. Simulation results are assessed by considering a comparison with some state-of-the-art methods, including FOMPA, MID, and 4S-DT. The results show that the proposed method with 97.08% accuracy and 97.29% precision provides the highest accuracy and reliability compared with the other studied methods. Moreover, the results show that the proposed method with a 97.1% sensitivity rate has the highest ratio. And finally, the proposed method with a 97.47% F 1 -score rate gives the uppermost value compared to the others.

Medical Image Enhancement Using Edge Information-Based Methods

2017 ◽  
pp. 123-148
Author(s):  
S. Anand
Keyword(s):  
Image Enhancement ◽  
Medical Image ◽  
Hyperbolic Function ◽  
Edge Information ◽  
X Ray ◽  
Enhancement Method ◽  
Chest X Ray ◽  
Medical Image Enhancement

Medical image enhancement improves the quality and facilitates diagnosis. This chapter investigates three methods of medical image enhancement by exploiting useful edge information. Since edges have higher perceptual importance, the edge information based enhancement process is always of interest. But determination of edge information is not an easy job. The edge information is obtained from various approaches such as differential hyperbolic function, Haar filters and morphological functions. The effectively determined edge information is used for enhancement process. The retinal image enhancement method given in this chapter improves the visual quality of the vessels in the optic region. X-ray image enhancement method presented here is to increase the visibility of the bones. These algorithms are used to enhance the computer tomography, chest x-ray, retinal, and mammogram images. These images are obtained from standard datasets and experimented. The performance of these enhancement methods are quantitatively evaluated.

scholarly journals Enlarge Medical Image using Line-Column Interpolation (LCI) Method

2018 ◽  
Vol 8 (5) ◽  
pp. 3620
Author(s):  
Jufriadif Na'am ◽  
Julius Santony ◽  
Yuhandri Yuhandri ◽  
Sumijan Sumijan ◽  
Gunadi Widi Nurcahyo
Keyword(s):  
Medical Image ◽  
Nearest Neighbor ◽  
Medical Images ◽  
Interpolation Method ◽  
Clinical Analysis ◽  
X Ray ◽  
Chest X Ray ◽  
Brain Ct Scan ◽  
Image Pixels

Quality of medical image has an important role in constructing right medical diagnosis. This paper recommends a method to improve the quality of medical images by increasing the size of the image pixels. By increasing the size of pixels, the size of the objects contained therein is also greater, making it easier to observe. In this study medical images of Brain CT-Scan, Chest X-Ray and Panoramic X-Ray were processed using Line-Column Interpolation (LCI) Method. The results of the treatment are then compared to Nearest Neighbor Interpolation (NNI), Bilinear Interpolation (BLI) and Bicubic Interpolation (BCI) processing results. The experiment shows that Line-Column Interpolation Method produces a larger image with details of the objects in it are not blurred and has equal visual effects. Thus, this method is expected to be a reference material in enlarging the size of the medical image for ease in clinical analysis.<br /><br />

scholarly journals CovidMulti-Net: A Parallel-Dilated Multi Scale Feature Fusion Architecture for the Identification of COVID-19 Cases from Chest X-ray Images

2021 ◽  
Author(s):  
Md. Saikat Islam Khan ◽  
Anichur Rahman ◽  
Md. Razaul Karim ◽  
Nasima Islam Bithi ◽  
Shahab Band ◽  
...  
Keyword(s):  
Healthcare Professionals ◽  
Feature Fusion ◽  
State Of The Art ◽  
Research Community ◽  
Diagnostic Model ◽  
X Ray ◽  
Multi Scale ◽  
The World ◽  
Chest X Ray ◽  
Learning Concept

The COVID-19 pandemic is an emerging respiratory infectious disease, having a significant impact on the health and life of many people around the world. Therefore, early identification of COVID-19 patients is the fastest way to restrain the spread of the pandemic. However, as the number of cases grows at an alarming pace, most developing countries are now facing a shortage of medical resources and testing kits. Besides, using testing kits to detect COVID-19 cases is a time-consuming, expensive, and cumbersome procedure. Faced with these obstacles, most physicians, researchers, and engineers have advocated for the advancement of computer-aided deep learning models to assist healthcare professionals in quickly and inexpensively recognize COVID-19 cases from chest X-ray (CXR) images. With this motivation, this paper proposes a CovidMulti-Net architecture based on the transfer learning concept to classify COVID-19 cases from normal and other pneumonia cases using three publicly available datasets that include 1341, 1341, and 446 CXR images from healthy samples and 902, 1564, and 1193 CXR images infected with Viral Pneumonia, Bacterial Pneumonia, and COVID-19 diseases. In the proposed framework, features from CXR images are extracted using three well-known pre-trained models, including DenseNet-169, ResNet-50, and VGG-19. The extracted features are then fed into a concatenate layer, making a robust hybrid model. The proposed framework achieved a classification accuracy of 99.4%, 95.2%, and 94.8% for 2-Class, 3-Class, and 4-Class datasets, exceeding all the other state-of-the-art models. These results suggest that the CovidMulti-Net frameworks ability to discriminate individuals with COVID-19 infection from healthy ones and provides the opportunity to be used as a diagnostic model in clinics and hospitals. We also made all the materials publicly accessible for the research community at: https://github.com/saikat15010/CovidMulti-Net-Architecture.git.

scholarly journals Identification of Pneumonia Disease Applying an Intelligent Computational Framework Based on Deep Learning and Machine Learning Techniques

2021 ◽  
Vol 2021 ◽  
pp. 1-20
Author(s):  
Yar Muhammad ◽  
Mohammad Dahman Alshehri ◽  
Wael Mohammed Alenazy ◽  
Truong Vinh Hoang ◽  
Ryan Alturki
Keyword(s):  
Machine Learning ◽  
Medical Image Analysis ◽  
Machine Learning Techniques ◽  
World Health ◽  
X Ray ◽  
Expert Radiologist ◽  
Learning Techniques ◽  
Effective Manner ◽  
The World ◽  
Chest X Ray

Pneumonia is a very common and fatal disease, which needs to be identified at the initial stages in order to prevent a patient having this disease from more damage and help him/her in saving his/her life. Various techniques are used for the diagnosis of pneumonia including chest X-ray, CT scan, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Medical image analysis plays a vital role in the diagnosis of various diseases like MERS, COVID-19, pneumonia, etc. and is considered to be one of the auspicious research areas. To analyze chest X-ray images accurately, there is a need for an expert radiologist who possesses expertise and experience in the desired domain. According to the World Health Organization (WHO) report, about 2/3 people in the world still do not have access to the radiologist, in order to diagnose their disease. This study proposes a DL framework to diagnose pneumonia disease in an efficient and effective manner. Various Deep Convolutional Neural Network (DCNN) transfer learning techniques such as AlexNet, SqueezeNet, VGG16, VGG19, and Inception-V3 are utilized for extracting useful features from the chest X-ray images. In this study, several machine learning (ML) classifiers are utilized. The proposed system has been trained and tested on chest X-ray and CT images dataset. In order to examine the stability and effectiveness of the proposed system, different performance measures have been utilized. The proposed system is intended to be beneficial and supportive for medical doctors to accurately and efficiently diagnose pneumonia disease.

Tutorial: Deep Learning Advancing the State-of-the-Art in Medical Image Analysis

2017 ◽  
pp. 6-7 ◽  
Author(s):  
Vincent Christlein ◽  
Florin C. Ghesu ◽  
Tobias Würfl ◽  
Andreas Maier ◽  
Fabian Isensee ◽  
...  
Keyword(s):  
Image Analysis ◽  
Deep Learning ◽  
Medical Image ◽  
State Of The Art ◽  
Medical Image Analysis ◽  
The State
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