Deep Learning Based Medical Image Analysis with Limited Data

Deep Learning Methods have shown its great effort in the area of Computer Vision. However, when solving the problems of medical imaging, deep learning’s power is confined by limited data available. We present a series of novel methodologies for solving medical imaging analysis problems with limited Computed tomography (CT) scans available. Our method, based on deep learning, with different strategies, including using Generative Adversar- ial Networks, two-stage training, infusing the expert knowledge, voting based or converting to other space, solves the data set limitation issue for the cur- rent medical imaging problems, specifically cancer detection and diagnosis, and shows very good performance and outperforms the state-of-art results in the literature. With the self-learned features, deep learning based techniques start to be applied to the biomedical imaging problems and various structures have been designed. In spite of its simplity and anticipated good performance, the deep learning based techniques can not perform to its best extent due to the limited size of data sets for the medical imaging problems. On the other side, the traditional hand-engineered features based methods have been studied in the past decades and a lot of useful features have been found by these research for the task of detecting and diagnosing the pulmonary nod- ules on CT scans, but these methods are usually performed through a series of complicated procedures with manually empirical parameter adjustments. Our method significantly reduces the complications of the traditional proce- dures for pulmonary nodules detection, while retaining and even outperforming the state-of-art accuracy. Besides, we make contribution on how to convert low-dose CT image to full-dose CT so as to adapting current models on the newly-emerged low-dose CT data

A scalable neural network architecture for self-supervised tomographic image reconstruction

We present a lightweight and scalable artificial neural network architecture which is used to reconstruct a tomographic image from a given sinogram. A self-supervised learning approach is used where the network iteratively generates an image that is then converted into a sinogram using the Radon transform; this new sinogram is then compared with the sinogram from the experimental dataset using a combined mean absolute error and structural similarity index measure loss function to update the weights of the network accordingly. We demonstrate that the network is able to reconstruct images that are larger than 1024 × 1024. Furthermore, it is shown that the new network is able to reconstruct images of higher quality than conventional reconstruction algorithms, such as the filtered back projection and iterative algorithms (SART, SIRT, CGLS), when sinograms with angular undersampling are used. The network is tested with simulated data as well as experimental synchrotron X-ray micro-tomography and X-ray diffraction computed tomography data

Deep Functional Mapping For Predicting Cancer Outcome

The effective understanding of the biological behavior and prognosis of cancer subtypes is becoming very important in-patient administration. Cancer is a diverse disorder in which a significant medical progression and diagnosis for each subtype can be observed and characterized. Computer-aided diagnosis for early detection and diagnosis of many kinds of diseases has evolved in the last decade. In this research, we address challenges associated with multi-organ disease diagnosis and recommend numerous models for enhanced analysis. We concentrate on evaluating the Magnetic Resonance Imaging (MRI), Computed Tomography (CT), and Positron Emission Tomography (PET) for brain, lung, and breast scans to detect, segment, and classify types of cancer from biomedical images. Moreover, histopathological, and genomic classification of cancer prognosis has been considered for multi-organ disease diagnosis and biomarker recommendation. We considered multi-modal, multi-class classification during this study. We are proposing implementing deep learning techniques based on Convolutional Neural Network and Generative Adversarial Network. In our proposed research we plan to demonstrate ways to increase the performance of the disease diagnosis by focusing on a combined diagnosis of histology, image processing, and genomics. It has been observed that the combination of medical imaging and gene expression can effectively handle the cancer detection situation with a higher diagnostic rate rather than considering the individual disease diagnosis. This research puts forward a blockchain-based system that facilitates interpretations and enhancements pertaining to automated biomedical systems. In this scheme, a secured sharing of the biomedical images and gene expression has been established. To maintain the secured sharing of the biomedical contents in a distributed system or among the hospitals, a blockchain-based algorithm is considered that generates a secure sequence to identity a hash key. This adaptive feature enables the algorithm to use multiple data types and combines various biomedical images and text records. All data related to patients, including identity, pathological records are encrypted using private key cryptography based on blockchain architecture to maintain data privacy and secure sharing of the biomedical contents

Denoising computed tomography images with 3D-convolution based neural networks

Abstract. Low-dose computed tomography (CT) is an imaging technique used in imaging cross-sectional images of the body that minimizes the radiation dose of the patient. Low-dose CT results in larger amounts of noise in the image and therefore in loss of information. Different denoising methods are used to try to reduce the noise corrupting the images. The aim of this thesis is to research if the temporal correlation of noise between the slices of the computed tomography volumes could be utilized in the denoising of the scans. A convolutional neural network with three-dimensional convolutional layers is trained using publicly available CT images. The images were injected with artificial noise simulating low-dose CT scans. Another network using two-dimensional convolutional layers was also trained for comparison. Different metrics were measured from results of a test dataset to determine the effect of denoising. The results indicate that utilizing the temporal information of the slices by three-dimensional convolutional layers is especially good in denoising of extremely low-dose CT scans. The denoising results between the different methods were closer to each other when the noise level was lower.Tietokonetomografiakuvien kohinanpoisto käyttäen 3D-konvoluutiopohjaisia neuroverkkoja. Tiivistelmä. Matalan säteilyannoksen tietokonetomografia on kuvantamismenetelmä, jolla saadaan kuvattua läpileikkauskuvia kehosta samalla minimoiden potilaan säteilyannosta. Matalan annoksen tietokonetomografia johtaa suurempaan kohinaan kuvassa ja täten informaation katoamiseen. Erilaisia kohinanpoistometodeja käytetään pyrkiessä pienentämään kuvien kohinaa. Tämän työn tarkoituksena oli tutkia, voitaisiinko tietokonetomografiakuvien viipaleiden kohinan välistä temporaalista informaatiota käyttää skannauksien kohinanpoistossa. Kolmiulotteisia konvoluutiotasoja käyttävä konvoluutioneuroverkko koulutettiin julkisesti saatavilla olevilla tietokonetomografiakuvilla. Kuviin oli asetettu keinotekoista kohinaa, simuloidakseen matalan annoksen tietokonetomografiakuvia. Toinen neuroverkko, jossa oli kaksiulotteisia konvoluutiotasoja, koulutettiin vertailua varten. Kohinanpoiston arvioimiseen mitattiin erilaisia metriikoita testidatasetistä saaduista tuloksista. Tulokset osoittavat, että viipaleiden välisen temporaalisen informaation käyttäminen kolmiulotteisten konvoluutiotasojen avulla on erityisen hyvä todella matalan annoksen tietokonetomografiakuvien kohinanpoistossa. Eri metodeilla saatujen kohinanpoiston tulosten väliset erot olivat pienempiä, kun kohinan taso oli matalamp

Computed-Tomography (CT) Scan

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (cross-sectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction

Recent Progress in Transformer-based Medical Image Analysis

The transformer is primarily used in the field of natural language processing. Recently, it has been adopted and shows promise in the computer vision (CV) field. Medical image analysis (MIA), as a critical branch of CV, also greatly benefits from this state-of-the-art technique. In this review, we first recap the core component of the transformer, the attention mechanism, and the detailed structures of the transformer. After that, we depict the recent progress of the transformer in the field of MIA. We organize the applications in a sequence of different tasks, including classification, segmentation, captioning, registration, detection, enhancement, localization, and synthesis. The mainstream classification and segmentation tasks are further divided into eleven medical image modalities. A large number of experiments studied in this review illustrate that the transformer-based method outperforms existing methods through comparisons with multiple evaluation metrics. Finally, we discuss the open challenges and future opportunities in this field. This task-modality review with the latest contents, detailed information, and comprehensive comparison may greatly benefit the broad MIA community.Comment: Computers in Biology and Medicine Accepte