Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Computational models for predicting liver toxicity in the deep learning era

Drug-induced liver injury (DILI) is a severe adverse reaction caused by drugs and may result in acute liver failure and even death. Many efforts have centered on mitigating risks associated with potential DILI in humans. Among these, quantitative structure-activity relationship (QSAR) was proven to be a valuable tool for early-stage hepatotoxicity screening. Its advantages include no requirement for physical substances and rapid delivery of results. Deep learning (DL) made rapid advancements recently and has been used for developing QSAR models. This review discusses the use of DL in predicting DILI, focusing on the development of QSAR models employing extensive chemical structure datasets alongside their corresponding DILI outcomes. We undertake a comprehensive evaluation of various DL methods, comparing with those of traditional machine learning (ML) approaches, and explore the strengths and limitations of DL techniques regarding their interpretability, scalability, and generalization. Overall, our review underscores the potential of DL methodologies to enhance DILI prediction and provides insights into future avenues for developing predictive models to mitigate DILI risk in humans

Parametric Scattering Networks

La plupart des percées dans l'apprentissage profond et en particulier dans les réseaux de neurones convolutifs ont impliqué des efforts importants pour collecter et annoter des quantités massives de données. Alors que les mégadonnées deviennent de plus en plus répandues, il existe de nombreuses applications où la tâche d'annoter plus d'un petit nombre d'échantillons est irréalisable, ce qui a suscité un intérêt pour les tâches d'apprentissage sur petits échantillons. Il a été montré que les transformées de diffusion d'ondelettes sont efficaces dans le cadre de données annotées limitées. La transformée de diffusion en ondelettes crée des invariants géométriques et une stabilité de déformation. Les filtres d'ondelettes utilisés dans la transformée de diffusion sont généralement sélectionnés pour créer une trame serrée via une ondelette mère paramétrée. Dans ce travail, nous étudions si cette construction standard est optimale. En nous concentrant sur les ondelettes de Morlet, nous proposons d'apprendre les échelles, les orientations et les rapports d'aspect des filtres. Nous appelons notre approche le Parametric Scattering Network. Nous illustrons que les filtres appris par le réseau de diffusion paramétrique peuvent être interprétés en fonction de la tâche spécifique sur laquelle ils ont été entrainés. Nous démontrons également empiriquement que notre transformée de diffusion paramétrique partage une stabilité aux déformations similaire à la transformée de diffusion traditionnelle. Enfin, nous montrons que notre version apprise de la transformée de diffusion génère des gains de performances significatifs par rapport à la transformée de diffusion standard lorsque le nombre d'échantillions d'entrainement est petit. Nos résultats empiriques suggèrent que les constructions traditionnelles des ondelettes ne sont pas toujours nécessaires.Most breakthroughs in deep learning have required considerable effort to collect massive amounts of well-annotated data. As big data becomes more prevalent, there are many applications where annotating more than a small number of samples is impractical, leading to growing interest in small sample learning tasks and deep learning approaches towards them. Wavelet scattering transforms have been shown to be effective in limited labeled data settings. The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yield more discriminative representations than other non-learned representations and to outperform learned representations in certain tasks, particularly on limited labeled data and highly structured signals. The wavelet filters used in the scattering transform are typically selected to create a tight frame via a parameterized mother wavelet. In this work, we investigate whether this standard wavelet filterbank construction is optimal. Focusing on Morlet wavelets, we propose to learn the scales, orientations, and aspect ratios of the filters to produce problem-specific parameterizations of the scattering transform. We call our approach the Parametric Scattering Network. We illustrate that filters learned by parametric scattering networks can be interpreted according to the specific task on which they are trained. We also empirically demonstrate that our parametric scattering transforms share similar stability to deformations as the traditional scattering transforms. We also show that our approach yields significant performance gains in small-sample classification settings over the standard scattering transform. Moreover, our empirical results suggest that traditional filterbank constructions may not always be necessary for scattering transforms to extract useful representations

Quantification of liver fibrosis—a comparative study

Liver disease has been targeted as the fifth most common cause of death worldwide and tends to steadily rise. In the last three decades, several publications focused on the quantification of liver fibrosis by means of the estimation of the collagen proportional area (CPA) in liver biopsies obtained from digital image analysis (DIA). In this paper, early and recent studies on this topic have been reviewed according to these research aims: the datasets used for the analysis, the employed image processing techniques, the obtained results, and the derived conclusions. The purpose is to identify the major strengths and “gray-areas” in the landscape of this topic

Biomedical Image Processing and Classification

Biomedical image processing is an interdisciplinary field involving a variety of disciplines, e.g., electronics, computer science, physics, mathematics, physiology, and medicine. Several imaging techniques have been developed, providing many approaches to the study of the human body. Biomedical image processing is finding an increasing number of important applications in, for example, the study of the internal structure or function of an organ and the diagnosis or treatment of a disease. If associated with classification methods, it can support the development of computer-aided diagnosis (CAD) systems, which could help medical doctors in refining their clinical picture

MACHINE LEARNING APPROACHES FOR BIOMARKER IDENTIFICATION AND SUBGROUP DISCOVERY FOR POST-TRAUMATIC STRESS DISORDER

Post-traumatic stress disorder (PTSD) is a psychiatric disorder caused by environmental and genetic factors resulting from alterations in genetic variation, epigenetic changes and neuroimaging characteristics. There is a pressing need to identify reliable molecular and physiological biomarkers for accurate diagnosis, prognosis, and treatment, as well to deepen the understanding of PTSD pathophysiology. Machine learning methods are widely used to infer patterns from biological data, identify biomarkers, and make predictions. The objective of this research is to apply machine learning methods for the accurate classification of human diseases from genome-scale datasets, focusing primarily on PTSD.The DoD-funded Systems Biology of PTSD Consortium has recruited combat veterans with and without PTSD for measurement of molecular and physiological data from blood or urine samples with the goal of identifying accurate and specific PTSD biomarkers. As a member of the Consortium with access to these PTSD multiple omics datasets, we first completed a project titled Clinical Subgroup-Specific PTSD Classification and Biomarker Discovery. We applied machine learning approaches to these data to build classification models consisting of molecular and clinical features to predict PTSD status. We also identified candidate biomarkers for diagnosis, which improves our understanding of PTSD pathogenesis. In a second project, entitled Multi-Omic PTSD Subgroup Identification and Clinical Characterization, we applied methods for integrating multiple omics datasets to investigate the complex, multivariate nature of the biological systems underlying PTSD. We identified an optimal 2 PTSD subgroups using two different machine learning approaches from 82 PTSD positive samples, and we found that the subgroups exhibited different remitting behavior as inferred from subjects recalled at a later time point. The results from our association, differential expression, and classification analyses demonstrated the distinct clinical and molecular features characterizing these subgroups.Taken together, our work has advanced our understanding of PTSD biomarkers and subgroups through the use of machine learning approaches. Results from our work should strongly contribute to the precise diagnosis and eventual treatment of PTSD, as well as other diseases. Future work will involve continuing to leverage these results to enable precision medicine for PTSD

Deep learning-based diagnostic system for malignant liver detection

Cancer is the second most common cause of death of human beings, whereas liver cancer is the fifth most common cause of mortality. The prevention of deadly diseases in living beings requires timely, independent, accurate, and robust detection of ailment by a computer-aided diagnostic (CAD) system. Executing such intelligent CAD requires some preliminary steps, including preprocessing, attribute analysis, and identification. In recent studies, conventional techniques have been used to develop computer-aided diagnosis algorithms. However, such traditional methods could immensely affect the structural properties of processed images with inconsistent performance due to variable shape and size of region-of-interest. Moreover, the unavailability of sufficient datasets makes the performance of the proposed methods doubtful for commercial use. To address these limitations, I propose novel methodologies in this dissertation. First, I modified a generative adversarial network to perform deblurring and contrast adjustment on computed tomography (CT) scans. Second, I designed a deep neural network with a novel loss function for fully automatic precise segmentation of liver and lesions from CT scans. Third, I developed a multi-modal deep neural network to integrate pathological data with imaging data to perform computer-aided diagnosis for malignant liver detection. The dissertation starts with background information that discusses the proposed study objectives and the workflow. Afterward, Chapter 2 reviews a general schematic for developing a computer-aided algorithm, including image acquisition techniques, preprocessing steps, feature extraction approaches, and machine learning-based prediction methods. The first study proposed in Chapter 3 discusses blurred images and their possible effects on classification. A novel multi-scale GAN network with residual image learning is proposed to deblur images. The second method in Chapter 4 addresses the issue of low-contrast CT scan images. A multi-level GAN is utilized to enhance images with well-contrast regions. Thus, the enhanced images improve the cancer diagnosis performance. Chapter 5 proposes a deep neural network for the segmentation of liver and lesions from abdominal CT scan images. A modified Unet with a novel loss function can precisely segment minute lesions. Similarly, Chapter 6 introduces a multi-modal approach for liver cancer variants diagnosis. The pathological data are integrated with CT scan images to diagnose liver cancer variants. In summary, this dissertation presents novel algorithms for preprocessing and disease detection. Furthermore, the comparative analysis validates the effectiveness of proposed methods in computer-aided diagnosis