Rethinking Medical Report Generation: Disease Revealing Enhancement with Knowledge Graph

Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases

The time-fractional mZK equation for gravity solitary waves and solutions using sech-tanh and radial basic function method

In recent years, we know that gravity solitary waves have gradually become the research spots and aroused extensive attention; on the other hand, the fractional calculus have been applied to the biology, optics and other fields, and it also has attracted more and more attention. In the paper, by employing multi-scale analysis and perturbation methods, we derive a new modified Zakharov–Kuznetsov (mZK) equation to describe the propagation features of gravity solitary waves. Furthermore, based on semi-inverse and Agrawal methods, the integer-order mZK equation is converted into the time-fractional mZK equation. In the past, fractional calculus was rarely used in ocean and atmosphere studies. Now, the study on nonlinear fluctuations of the gravity solitary waves is a hot area of research by using fractional calculus. It has potential value for deep understanding of the real ocean–atmosphere. Furthermore, by virtue of the sech-tanh method, the analytical solution of the time-fractional mZK equation is obtained. Next, using the above analytical solution, a numerical solution of the time-fractional mZK equation is given by using radial basis function method. Finally, the effect of time-fractional order on the wave propagation is explained. &nbsp

MicroRNA-126 regulates the expression of inflammationrelated genes in vascular smooth muscle cells of diabetic rats

Purpose: To investigate the regulatory role of miR-126 in inflammation-associated gene expression in vascular smooth muscle (VSMCs) cells of diabetic rats.Methods: Diabetes was induced in rats by intraperitoneal injection of streptozocin (STZ) at a dose of 50 mg/kg. VSMCs were isolated from the aortic intimal-medial layers of the rats by a standard protocol. Expression of miR-126 was determined by quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting. Transfection was performed with Lipofectamine 2000 reagent.Results: Expression of miR-126 was significantly (p < 0.05) downregulated in the VSMCs of diabetic rats. Similarly, expressions of SOD, APX and CAT were downregulated, while those of COX, LOX and NOS were significantly upregulated in VSMCs. However, transfection-induced miR-126 overexpression in the VSMCs of diabetic rats led to significant (p < 0.05) upregulation of SOD, CAT, APX and downregulation of COX, LOX and NOS. TargetScan analysis revealed that miR-126 exerted these effects by targeting SIRT1 gene. Furthermore, qRT-PCR and western blotting revealed that miR-126 overexpression in diabetic VMSCs caused significant (p < 0.05) upregulation of the expression of SIRT-1.Conclusion: The results indicate that miR-126 regulates the expression of inflammation-related genes by targeting SIRT-1 genes in vascular smooth muscle cells of diabetic rats. Thus, miR-126 may be beneficial in the management of diabetes.Keywords: Diabetes, Inflammation, Genes, microRNA, vascular smooth muscle cell

The Intrinsic Manifolds of Radiological Images and their Role in Deep Learning

The manifold hypothesis is a core mechanism behind the success of deep learning, so understanding the intrinsic manifold structure of image data is central to studying how neural networks learn from the data. Intrinsic dataset manifolds and their relationship to learning difficulty have recently begun to be studied for the common domain of natural images, but little such research has been attempted for radiological images. We address this here. First, we compare the intrinsic manifold dimensionality of radiological and natural images. We also investigate the relationship between intrinsic dimensionality and generalization ability over a wide range of datasets. Our analysis shows that natural image datasets generally have a higher number of intrinsic dimensions than radiological images. However, the relationship between generalization ability and intrinsic dimensionality is much stronger for medical images, which could be explained as radiological images having intrinsic features that are more difficult to learn. These results give a more principled underpinning for the intuition that radiological images can be more challenging to apply deep learning to than natural image datasets common to machine learning research. We believe rather than directly applying models developed for natural images to the radiological imaging domain, more care should be taken to developing architectures and algorithms that are more tailored to the specific characteristics of this domain. The research shown in our paper, demonstrating these characteristics and the differences from natural images, is an important first step in this direction.Comment: preprint version, accepted for MICCAI 2022 (25th International Conference on Medical Image Computing and Computer Assisted Intervention). 8 pages (+ author names + references + supplementary), 4 figures. Code available at https://github.com/mazurowski-lab/radiologyintrinsicmanifold

A systematic study of the foreground-background imbalance problem in deep learning for object detection

The class imbalance problem in deep learning has been explored in several studies, but there has yet to be a systematic analysis of this phenomenon in object detection. Here, we present comprehensive analyses and experiments of the foreground-background (F-B) imbalance problem in object detection, which is very common and caused by small, infrequent objects of interest. We experimentally study the effects of different aspects of F-B imbalance (object size, number of objects, dataset size, object type) on detection performance. In addition, we also compare 9 leading methods for addressing this problem, including Faster-RCNN, SSD, OHEM, Libra-RCNN, Focal-Loss, GHM, PISA, YOLO-v3, and GFL with a range of datasets from different imaging domains. We conclude that (1) the F-B imbalance can indeed cause a significant drop in detection performance, (2) The detection performance is more affected by F-B imbalance when fewer training data are available, (3) in most cases, decreasing object size leads to larger performance drop than decreasing number of objects, given the same change in the ratio of object pixels to non-object pixels, (6) among all selected methods, Libra-RCNN and PISA demonstrate the best performance in addressing the issue of F-B imbalance. (7) When the training dataset size is large, the choice of method is not impactful (8) Soft-sampling methods, including focal-loss, GHM, and GFL, perform fairly well on average but are relatively unstable

Knowledge Restore and Transfer for Multi-label Class-Incremental Learning

Current class-incremental learning research mainly focuses on single-label classification tasks while multi-label class-incremental learning (MLCIL) with more practical application scenarios is rarely studied. Although there have been many anti-forgetting methods to solve the problem of catastrophic forgetting in class-incremental learning, these methods have difficulty in solving the MLCIL problem due to label absence and information dilution. In this paper, we propose a knowledge restore and transfer (KRT) framework for MLCIL, which includes a dynamic pseudo-label (DPL) module to restore the old class knowledge and an incremental cross-attention(ICA) module to save session-specific knowledge and transfer old class knowledge to the new model sufficiently. Besides, we propose a token loss to jointly optimize the incremental cross-attention module. Experimental results on MS-COCO and PASCAL VOC datasets demonstrate the effectiveness of our method for improving recognition performance and mitigating forgetting on multi-label class-incremental learning tasks

Research progress of MicroRNA in podocytes autophagy in diabetic nephropathy

Diabetic nephropathy (DN) is one of the most common microvascular complications of diabetes, and is a kind of abnormal microangiopathy of kidney structure, function or clinical indicators caused by diabetes. Podocyte injury has been considered as a major contributor to the progression of diabetic nephropathy(DN). microRNA can participate in podocytes injury through autophagy.In this paper, the mechanism of microRNA involved in DN podocytes autophagy was reviewed to provide reference for the treatment of DN in the future