Tensor Denoising via Amplification and Stable Rank Methods

Tensors in the form of multilinear arrays are ubiquitous in data science applications. Captured real-world data, including video, hyperspectral images, and discretized physical systems, naturally occur as tensors and often come with attendant noise. Under the additive noise model and with the assumption that the underlying clean tensor has low rank, many denoising methods have been created that utilize tensor decomposition to effect denoising through low rank tensor approximation. However, all such decomposition methods require estimating the tensor rank, or related measures such as the tensor spectral and nuclear norms, all of which are NP-hard problems. In this work we leverage our previously developed framework of $\textit{tensor amplification}$, which provides good approximations of the spectral and nuclear tensor norms, to denoising synthetic tensors of various sizes, ranks, and noise levels, along with real-world tensors derived from physiological signals. We also introduce two new notions of tensor rank -- $\textit{stable slice rank}$ and $\textit{stable }$$X$$\textit{-rank}$ -- and new denoising methods based on their estimation. The experimental results show that in the low rank context, tensor-based amplification provides comparable denoising performance in high signal-to-noise ratio (SNR) settings and superior performance in noisy (i.e., low SNR) settings, while the stable $X$-rank method achieves superior denoising performance on the physiological signal data

Unified wavelet and gaussian filtering for segmentation of CT images; application in segmentation of bone in pelvic CT images

Background The analysis of pelvic CT scans is a crucial step for detecting and assessing the severity of Traumatic Pelvic Injuries. Automating the processing of pelvic CT scans could impact decision accuracy, decrease the time for decision making, and reduce health care cost. This paper discusses a method to automate the segmentation of bone from pelvic CT images. Accurate segmentation of bone is very important for developing an automated assisted-decision support system for Traumatic Pelvic Injury diagnosis and treatment. Methods The automated method for pelvic CT bone segmentation is a hierarchical approach that combines filtering and histogram equalization, for image enhancement, wavelet analysis and automated seeded region growing. Initial results of segmentation are used to identify the region where bone is present and to target histogram equalization towards the specific area. Speckle Reducing Anisotropic Didffusion (SRAD) filter is applied to accentuate the desired features in the region. Automated seeded region growing is performed to refine the initial bone segmentation results. Results The proposed method automatically processes pelvic CT images and produces accurate segmentation. Bone connectivity is achieved and the contours and sizes of bones are true to the actual contour and size displayed in the original image. Results are promising and show great potential for fracture detection and assessing hemorrhage presence and severity. Conclusion Preliminary experimental results of the automated method show accurate bone segmentation. The novelty of the method lies in the unique hierarchical combination of image enhancement and segmentation methods that aims at maximizing the advantages of the combined algorithms. The proposed method has the following advantages: it produces accurate bone segmentation with maintaining bone contour and size true to the original image and is suitable for automated bone segmentation from pelvic CT images

A Novel Tropical Geometry-based Interpretable Machine Learning Method: Pilot Application to Delivery of Advanced Heart Failure Therapies

Abstract—A model’s interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to demonstrate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, we present a pilot application in identifying heart failure patients that are eligible for advanced therapies as proof of principle. From our results on this particular application, the proposed network achieves the highest F1 score. The network is capable of learning rules that can be interpreted and used by clinical providers. In addition, existing fuzzy domain knowledge can be easily transferred into the network and facilitate model training. In our application, with the existing knowledge, the F1 score was improved by over 5%. The characteristics of the proposed network make it promising in applications requiring model reliability and justification

Book Reviews: Spring 2020

Book Reviews, of:Dalsgaard (ed) – Thomas Pynchon in ContextChetwynd, Freer, Maragos (eds) – Thomas Pynchon, Sex, and GenderMogultay – The Ruins of Urban Modernity: Thomas Pynchon's Against the DayAlworth – Site Reading: Fiction, Art, Social FormMullins – Postmodernism in Pieces: Materializing the SocialHenry – New Media and the Transformation of Postmodern American Literature: From Cage to Connectionden Dulk – Existentialist Engagement in Wallace, Eggers, and Foer: A Philosophical Analysis of Contemporary American LiteratureAnderson – Postmodern Artistry in Medievalist Fiction: An International StudyHouser – Ecosickness in Contemporary U.S. Fiction: Environment and AffectPalleau-Papin (ed) – Under Fire; William T. Vollmann, The Rifles: A Critical Study [a note from the Book Reviews Editor: if you're interested in reviewing a book on any aspect of unconventional post-1945 US literature, please send an email proposing a review to [email protected]

Images of modernist fiction: literary and pictorial narrative from Joyce to Spiegelman

This project develops a new context for understanding the rise of the graphic novel by linking the grammar of comics as a form to the multimedia experiments of modernist writing. Presenting a counter-history of comics, I show how the form can be traced not just to the emergence of newspaper comics, as critics routinely claim, but also to the intermedial strain of modernist avant-garde fiction. With developments in photomechanical engraving and offset lithography, the space of the printed page increasingly became a zone of uncertain contact between text and image. Taking up a number of different genres—philosophy, modernist prose, book illustrations, and the wordless novel in woodcuts—I argue that modernist writing and early experiments in graphic narrative alike responded to this transformation of the book’s materiality by imagining new modes of literary and pictorial storytelling.  While comics scholars often trace the rise of the graphic novel to the work of Winsor McCay, George Herriman, and other early-century newspaper cartoonists, I offer a history and an aesthetics of graphic narrative that takes full account of the medium’s roots in the debates and collaborative practices of the modernist avant-garde. Graphic narratives undoubtedly take their grammar—panels, word balloons, motion lines—from newspaper comics. But they also become aesthetically entangled with the intermedial dimension of modernist writing. Literary modernism, I argue, is not a high cultural tradition that graphic novelists want to attack and overturn. It is instead a tradition that foregrounded the questions about media, language, and narrative that dominate contemporary graphic fiction. Exploring the dynamic connection between modernists like Joyce, Matisse, Rockwell Kent, and Lynn Ward, this project seeks to uncover the buried history of modernism’s kinship with the graphic novel, a kinship based on the legacy of inter-art experimentation that characterizes both modernist fiction and graphic narrative.2022-10-09T00:00:00

Automated Spleen Injury Detection Using 3D Active Contours and Machine Learning

The spleen is one of the most frequently injured organs in blunt abdominal trauma. Computed tomography (CT) is the imaging modality of choice to assess patients with blunt spleen trauma, which may include lacerations, subcapsular or parenchymal hematomas, active hemorrhage, and vascular injuries. While computer-assisted diagnosis systems exist for other conditions assessed using CT scans, the current method to detect spleen injuries involves the manual review of scans by radiologists, which is a time-consuming and repetitive process. In this study, we propose an automated spleen injury detection method using machine learning. CT scans from patients experiencing traumatic injuries were collected from Michigan Medicine and the Crash Injury Research Engineering Network (CIREN) dataset. Ninety-nine scans of healthy and lacerated spleens were split into disjoint training and test sets, with random forest (RF), naive Bayes, SVM, k-nearest neighbors (k-NN) ensemble, and subspace discriminant ensemble models trained via 5-fold cross validation. Of these models, random forest performed the best, achieving an Area Under the receiver operating characteristic Curve (AUC) of 0.91 and an F1 score of 0.80 on the test set. These results suggest that an automated, quantitative assessment of traumatic spleen injury has the potential to enable faster triage and improve patient outcomes