A representational framework and user-interface for an image understanding workstation

Problems in image understanding involve a wide variety of data (e.g., image arrays, edge maps, 3-D shape models) and processes or algorithms (e.g., convolution, feature extraction, rendering). The underlying structure of an Image Understanding Workstation designed to support mulitple levels and types of representations for both data and processes is described, also the user-interface. The Image Understanding Workstation consists of two parts: the Image Understanding (IU) Framework, and the user-interface. The IU Framework is the set of data and process representations. It includes multiple levels of representation for data such as images (2-D), sketches (2-D), surfaces (2 1/2 D), and models (3-D). The representation scheme for processes characterizes their inputs, outputs, and parameters. Data and processes may reside on different classes of machines. The user-interface to the IU Workstation gives the user convenient access for creating, manipulating, transforming, and displaying image data. The user-interface follows the structure of the IU Framework and gives the user control over multiple types of data and processes. Both the IU Framework and user-interface are implemented on a LISP machine

In Defence of Proportionalism

In his book Slaves of the Passions, Mark Schroeder defends a Humean theory of reasons. Humeanism is the view that you have a reason to X only if X‐ing promotes at least one of your desires. But Schroeder rejects a natural companion theory of the weight of reasons, which he calls proportionalism. According to it, the weight of a reason is proportionate to the strength of the desire that grounds it and the extent to which the act promotes the object of that desire. In this paper, I aim to do three things: to show why Schroeder's arguments against proportionalism do not refute it; to identify the real trouble with proportionalism; and to suggest a better way of understanding it. According to this theory, the overall strength of reasons is determined by the agent's preferences

Quantifying the Preferential Direction of the Model Gradient in Adversarial Training With Projected Gradient Descent

Adversarial training, especially projected gradient descent (PGD), has been the most successful approach for improving robustness against adversarial attacks. After adversarial training, gradients of models with respect to their inputs have a preferential direction. However, the direction of alignment is not mathematically well established, making it difficult to evaluate quantitatively. We propose a novel definition of this direction as the direction of the vector pointing toward the closest point of the support of the closest inaccurate class in decision space. To evaluate the alignment with this direction after adversarial training, we apply a metric that uses generative adversarial networks to produce the smallest residual needed to change the class present in the image. We show that PGD-trained models have a higher alignment than the baseline according to our definition, that our metric presents higher alignment values than a competing metric formulation, and that enforcing this alignment increases the robustness of models.Comment: Updates for second version: added methods/analysis for multiclass datasets; added new references found since last submission; removed claims about interpretability; overall editin

Interpretation of Disease Evidence for Medical Images Using Adversarial Deformation Fields

The high complexity of deep learning models is associated with the difficulty of explaining what evidence they recognize as correlating with specific disease labels. This information is critical for building trust in models and finding their biases. Until now, automated deep learning visualization solutions have identified regions of images used by classifiers, but these solutions are too coarse, too noisy, or have a limited representation of the way images can change. We propose a novel method for formulating and presenting spatial explanations of disease evidence, called deformation field interpretation with generative adversarial networks (DeFI-GAN). An adversarially trained generator produces deformation fields that modify images of diseased patients to resemble images of healthy patients. We validate the method studying chronic obstructive pulmonary disease (COPD) evidence in chest x-rays (CXRs) and Alzheimer's disease (AD) evidence in brain MRIs. When extracting disease evidence in longitudinal data, we show compelling results against a baseline producing difference maps. DeFI-GAN also highlights disease biomarkers not found by previous methods and potential biases that may help in investigations of the dataset and of the adopted learning methods.Comment: Accepted for MICCAI 202

Adversarial regression training for visualizing the progression of chronic obstructive pulmonary disease with chest x-rays

Knowledge of what spatial elements of medical images deep learning methods use as evidence is important for model interpretability, trustiness, and validation. There is a lack of such techniques for models in regression tasks. We propose a method, called visualization for regression with a generative adversarial network (VR-GAN), for formulating adversarial training specifically for datasets containing regression target values characterizing disease severity. We use a conditional generative adversarial network where the generator attempts to learn to shift the output of a regressor through creating disease effect maps that are added to the original images. Meanwhile, the regressor is trained to predict the original regression value for the modified images. A model trained with this technique learns to provide visualization for how the image would appear at different stages of the disease. We analyze our method in a dataset of chest x-rays associated with pulmonary function tests, used for diagnosing chronic obstructive pulmonary disease (COPD). For validation, we compute the difference of two registered x-rays of the same patient at different time points and correlate it to the generated disease effect map. The proposed method outperforms a technique based on classification and provides realistic-looking images, making modifications to images following what radiologists usually observe for this disease. Implementation code is available at https://github.com/ricbl/vrgan.Comment: Accepted for MICCAI 201

Nuclear epidermal growth factor receptor as a therapeutic target

Epidermal growth factor receptor (EGFR) is one of the most well-studied oncogenes with roles in proliferation, growth, metastasis, and therapeutic resistance. This intense study has led to the development of a range of targeted therapeutics including small-molecule tyrosine kinase inhibitors (TKIs), monoclonal antibodies, and nanobodies. These drugs are excellent at blocking the activation and kinase function of wild-type EGFR (wtEGFR) and several common EGFR mutants. These drugs have significantly improved outcomes for patients with cancers including head and neck, glioblastoma, colorectal, and non-small cell lung cancer (NSCLC). However, therapeutic resistance is often seen, resulting from acquired mutations or activation of compensatory signaling pathways. Additionally, these therapies are ineffective in tumors where EGFR is found predominantly in the nucleus, as can be found in triple negative breast cancer (TNBC). In TNBC, EGFR is subjected to alternative trafficking which drives the nuclear localization of the receptor. In the nucleus, EGFR interacts with several proteins to activate transcription, DNA repair, migration, and chemoresistance. Nuclear EGFR (nEGFR) correlates with metastatic disease and worse patient prognosis yet targeting its nuclear localization has proved difficult. This review provides an overview of current EGFR-targeted therapies and novel peptide-based therapies that block nEGFR, as well as their clinical applications and potential for use in oncology

Stapled EGFR peptide reduces inflammatory breast cancer and inhibits additional HER-driven models of cancer

Background: The human epidermal growth factor receptor (HER) family of transmembrane tyrosine kinases is overexpressed and correlates with poor prognosis and decreased survival in many cancers. The receptor family has been therapeutically targeted, yet tyrosine kinase inhibitors (TKIs) do not inhibit kinase-independent functions and antibody-based targeting does not affect internalized receptors. We have previously demonstrated that a peptide mimicking the internal juxtamembrane domain of HER1 (EGFR; EJ1) promotes the formation of non-functional HER dimers that inhibit kinase-dependent and kinase-independent functions of HER1 (ERBB1/EGFR), HER2 (ERBB2) and HER3 (ERBB3). Despite inducing rapid HER-dependent cell death in vitro, EJ1 peptides are rapidly cleared in vivo, limiting their efficacy. Method: To stabilize EJ1 activity, hydrocarbon staples (SAH) were added to the active peptide (SAH-EJ1), resulting in a 7.2-fold increase in efficacy and decreased in vivo clearance. Viability assays were performed across HER1 and HER2 expressing cell lines, therapeutic-resistant breast cancer cells, clinically relevant HER1-mutated lung cancer cells, and patient-derived glioblastoma cells, in all cases demonstrating improved efficacy over standard of care pan-HER therapeutics. Tumor burden studies were also performed in lung, glioblastoma, and inflammatory breast cancer mouse models, evaluating tumor growth and overall survival. Results: When injected into mouse models of basal-like and inflammatory breast cancers, EGFRvIII-driven glioblastoma, and lung adenocarcinoma with Erlotinib resistance, tumor growth is inhibited and overall survival is extended. Studies evaluating the toxicity of SAH-EJ1 also demonstrate a broad therapeutic window. Conclusions: Taken together, these data indicate that SAH-EJ1 may be an effective therapeutic for HER-driven cancers with the potential to eliminate triple negative inflammatory breast cancer.Arizona Cancer Therapeutics; Ginny L Clements Breast Cancer Research Fund; NIH [NIH 1R41CA203353]; NCI [P30 CA023074]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Local Evolutionary Debunking Arguments

Evolutionary debunking arguments in ethics aim to use facts about the evolutionary causes of ethical beliefs to undermine their justification. Global Evolutionary Debunking Arguments (GDAs) are arguments made in metaethics that aim to undermine the justification of all ethical beliefs. Local Evolutionary Debunking Arguments (LDAs) are arguments made in first‐order normative ethics that aim to undermine the justification of only some of our ethical beliefs. Guy Kahane, Regina Rini, Folke Tersman, and Katia Vavova argue for skepticism about the possibility of LDAs. They argue that LDAs cannot be successful because they over‐extend in a way that makes them self‐undermining and yield a form of moral skepticism. In this paper I argue that this skepticism about the possibility of LDAs is misplaced

Opposing Regulation of the EGF Receptor: A Molecular Switch Controlling Cytomegalovirus Latency and Replication

Herpesviruses persist indefinitely in their host through complex and poorly defined interactions that mediate latent, chronic or productive states of infection. Human cytomegalovirus (CMV or HCMV), a ubiquitous β-herpesvirus, coordinates the expression of two viral genes, UL135 and UL138, which have opposing roles in regulating viral replication. UL135 promotes reactivation from latency and virus replication, in part, by overcoming replication-suppressive effects of UL138. The mechanism by which UL135 and UL138 oppose one another is not known. We identified viral and host proteins interacting with UL138 protein (pUL138) to begin to define the mechanisms by which pUL135 and pUL138 function. We show that pUL135 and pUL138 regulate the viral cycle by targeting that same receptor tyrosine kinase (RTK) epidermal growth factor receptor (EGFR). EGFR is a major homeostatic regulator involved in cellular proliferation, differentiation, and survival, making it an ideal target for viral manipulation during infection. pUL135 promotes internalization and turnover of EGFR from the cell surface, whereas pUL138 preserves surface expression and activation of EGFR. We show that activated EGFR is sequestered within the infection-induced, juxtanuclear viral assembly compartment and is unresponsive to stress. Intriguingly, these findings suggest that CMV insulates active EGFR in the cell and that pUL135 and pUL138 function to fine-tune EGFR levels at the cell surface to allow the infected cell to respond to extracellular cues. Consistent with the role of pUL135 in promoting replication, inhibition of EGFR or the downstream phosphoinositide 3-kinase (PI3K) favors reactivation from latency and replication. We propose a model whereby pUL135 and pUL138 together with EGFR comprise a molecular switch that regulates states of latency and replication in HCMV infection by regulating EGFR trafficking to fine tune EGFR signaling