A Survey of Medical Image Analysis Using Deep Learning Approaches

Rehman, Aasia; Butt, Muheet Ahmed; Zaman, Majid

doi:10.1109/iccmc51019.2021.9418385

Cited by 23 publications

(14 citation statements)

References 52 publications

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“…Considering this, Rehman et al [100] provided an updated overview of the advances on deep learning applied to medical image analysis. The survey was divided over the different pattern recognition tasks (image classification, segmentation, image registration).…”

Section: Deep Learning In Medical Image Analysismentioning

confidence: 99%

“…The works of Litjens et al [71] and Rehman et al [100] show that in the last years the use of deep learning has greatly improved the performance of medical imaging analysis algorithms, allowing also to create a myriad of approaches for the different image modalities and recognition tasks. Nevertheless, this contrasts with the adoption of these algorithms by clinicians who refuse to rely on decisions that they do not understand [69].…”

Section: Interpretable Deep Learning In Medical Imagingmentioning

confidence: 99%

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Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey

Patrício¹,

Neves²,

Teixeira³

2022

Preprint

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The remarkable success of deep learning has prompted interest in its application to medical diagnosis. Even tough state-ofthe-art deep learning models have achieved human-level accuracy on the classification of different types of medical data, these models are hardly adopted in clinical workflows, mainly due to their lack of interpretability. The black-box-ness of deep learning models has raised the need for devising strategies to explain the decision process of these models, leading to the creation of the topic of eXplainable Artificial Intelligence (XAI). In this context, we provide a thorough survey of XAI applied to medical diagnosis, including visual, textual, and example-based explanation methods. Moreover, this work reviews the existing medical imaging datasets and the existing metrics for evaluating the quality of the explanations . Complementary to most existing surveys, we include a performance comparison among a set of report generation-based methods. Finally, the major challenges in applying XAI to medical imaging are also discussed.CCS Concepts: • Applied computing → Health care information systems.

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Section: Deep Learning In Medical Image Analysismentioning

confidence: 99%

Section: Interpretable Deep Learning In Medical Imagingmentioning

confidence: 99%

Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey

Patrício¹,

Neves²,

Teixeira³

2022

Preprint

View full text Add to dashboard Cite

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“…Jesus Loarce-Martos, Francisco Xavier Leon-Roman, and Sandra Garrote-Corral talked on improvements in home spirometry and quantitative computer tomography for the diagnosis and treatment of interstitial lung illness linked to connective tissue diseases [20]. Aasia Rehman, Dr. Majid Zaman, and Dr. Muheet Ahmed Butt reported a range of medical imaging methods in their survey [21] Panfang Hua's [22] region growth approach is unreliable in high attenuation patterns like ILD, but it performs well in the presence of noise and can correctly distinguish areas with the same properties. Azar Tolouee devised a threshold method that chooses the best threshold to distinguish the lung region from the backdrop [23].…”

Section: Related Workmentioning

confidence: 99%

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Yadlapalli¹,

Bhavana

2022

SCPE

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Medical image processing involves using and examining 3D human body images, which are most frequently acquired through a computed tomography scanner, to diagnose disorders. Medical image process- ing helps radiologists, engineers, and clinicians better comprehend the anatomy of specific patients or groups of patients. Due to recent advancements in deep learn ing techniques, the study of medical image analysis is now a quickly expanding area of research. Interstitial Lung Disease is a chronic lung disease that worsens with time. This condition cannot be completely treated when the lungs have been damaged. Early detection, on the other hand, aids in the control of the disease. It causes lung scarring as a result. The first methodology characterizes lung tissue utilizing first order statistics, grey live occurrence, run length matrices, and fractal analysis. It was suggested by Uppaluri et al in one instance. In the pre-processing step, patients' CT scans are presented using various color map models for better understanding of data-set. and also for determining the patients final Force Vital Capacity and Confidence values using a Pytorch model with leaky relu activation function. These variables can be used to determine whether a person has a disease. Segmentation is a crucial stage in employing a computer assisted diagnosis system to estimate interstitial lung disease. Accurate segmentation of aberrant lung is essential for a trustworthy computer-aided illness diagnosis. Using separate training, validation, and test sets, we proposed an efficient deep learning model using Unet architecture and Densenet121 to segment lungs with Interstitial Lung Disease. The proposed segmentation model distinguishes the exact lung region from the ct slice background. To train and evaluate the algo rithm, 176 sparsely annotated Computed Tomography scans were utilized. The training was completed in a supervised and end to end manner. Contrary to current approaches, the suggested method yields accurate segmentation results without the requirement for re-initialization. We were able to achieve an accuracy of 92.59 percent after training the proposed model with Nvidia's CUDA GPU.

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“…These enable the generation and the distribution of several CXR datasets in recent years, providing the opportunity to develop a more sophisticated model architecture, i.e., deep learning models [12]. The data-driven nature of deep learning benefits from large, annotated datasets, and the growing quantity of publicly available CXR imaging datasets enables the building of highly accurate models [13]. Due to the superior performance of deep learning, it has become highly attractive for medical image analysis [10], [14], [15], [16], [17].…”

Section: Introductionmentioning

confidence: 99%

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

Termritthikun

Umer

Suwanwimolkul³

et al. 2024

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them less feasible for compact devices with low computational requirements. To overcome this problem, we propose a knowledge distillation (KD) strategy to create the compact deep learning model for the real-time multi-label CXR image classification. We study different alternatives of CNNs and Transforms as the teacher to distill the knowledge to a smaller student. Then, we employed explainable artificial intelligence (XAI) to provide the visual explanation for the model decision improved by the KD. Our results on three benchmark CXR datasets show that our KD strategy provides the improved performance on the compact student model, thus being the feasible choice for many limited hardware platforms. For instance, when using DenseNet161 as the teacher network, EEEA-Net-C2 achieved an AUC of 83.7%, 87.1%, and 88.7% on the ChestX-ray14, CheXpert, and PadChest datasets, respectively, with fewer parameters of 4.7 million and computational cost of 0.3 billion FLOPS.

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A Survey of Medical Image Analysis Using Deep Learning Approaches

Cited by 23 publications

References 52 publications

Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey

Explainable Deep Learning Methods in Medical Imaging Diagnosis: A Survey

Segmentation and Pre-processing of Interstitial Lung Disease using Deep Learning Model

Explainable Knowledge Distillation for On-Device Chest X-Ray Classification

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