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Pancreatic ductal adenocarcinoma (PDAC), the most deadly solid malignancy, is typically detected late and at an inoperable stage. Early or incidental detection is associated with prolonged survival, but screening asymptomatic individuals for PDAC using a single test remains unfeasible due to the low prevalence and potential harms of false positives. Non-contrast computed tomography (CT), routinely performed for clinical indications, offers the potential for large-scale screening, however, identification of PDAC using non-contrast CT has long been considered impossible. Here, we develop a deep learning approach, pancreatic cancer detection with artificial intelligence (PANDA), that can detect and classify pancreatic lesions with high accuracy via non-contrast CT. PANDA is trained on a dataset of 3,208 patients from a single center. PANDA achieves an area under the receiver operating characteristic curve (AUC) of 0.986–0.996 for lesion detection in a multicenter validation involving 6,239 patients across 10 centers, outperforms the mean radiologist performance by 34.1% in sensitivity and 6.3% in specificity for PDAC identification, and achieves a sensitivity of 92.9% and specificity of 99.9% for lesion detection in a real-world multi-scenario validation consisting of 20,530 consecutive patients. Notably, PANDA utilized with non-contrast CT shows non-inferiority to radiology reports (using contrast-enhanced CT) in the differentiation of common pancreatic lesion subtypes. PANDA could potentially serve as a new tool for large-scale pancreatic cancer screening. A deep learning model provides high accuracy in detecting pancreatic lesions in multicenter data, outperforming radiology specialists.

In the multimodal neuroimaging framework, data on a single subject are collected from inherently different sources such as functional MRI, structural MRI, behavioral and/or phenotypic information. The information each source provides is not independent; a subset of features from each modality maps to one or more common latent dimensions, which can be interpreted using generative models. These latent dimensions, or “topics,” provide a sparse summary of the generative process behind the features for each individual. Topic modeling, an unsupervised generative model, has been used to map seemingly disparate features to a common domain. We use Non-Negative Matrix Factorization (NMF) to infer the latent structure of multimodal ADHD data containing fMRI, MRI, phenotypic and behavioral measurements. We compare four different NMF algorithms and find that the sparsest decomposition is also the most differentiating between ADHD and healthy patients. We identify dimensions that map to interpretable, recognizable dimensions such as motion, default mode network activity, and other such features of the input data. For example, structural and functional graph theory features related to default mode subnetworks clustered with the ADHD-Inattentive diagnosis. Structural measurements of the default mode network (DMN) regions such as the posterior cingulate, precuneus, and parahippocampal regions were all related to the ADHD-Inattentive diagnosis. Ventral DMN subnetworks may have more functional connections in ADHD-I, while dorsal DMN may have less. ADHD topics are dependent upon diagnostic site, suggesting diagnostic differences across geographic locations. We assess our findings in light of the ADHD-200 classification competition, and contrast our unsupervised, nominated topics with previously published supervised learning methods. Finally, we demonstrate the validity of these latent variables as biomarkers by using them for classification of ADHD in 730 patients. Cumulatively, this manuscript addresses how multimodal data in ADHD can be interpreted by latent dimensions. •We identify latent dimensions (topics) in multimodal ADHD (fMRI, MRI, phenotypic).•We compare four different Non-negative Matrix Factoriation (NMF) algorithms.•The sparsest NMF algorithms discriminates best between ADHD and healthy subjects.•“Site” nominated within “topics” suggests ADHD diagnosis may differ by location.•One topic suggests differential changes in the default-mode subnetwork for ADHD.

This is an opinion paper about the strengths and weaknesses of Deep Nets for vision. They are at the heart of the enormous recent progress in artificial intelligence and are of growing importance in cognitive science and neuroscience. They have had many successes but also have several limitations and there is limited understanding of their inner workings. At present Deep Nets perform very well on specific visual tasks with benchmark datasets but they are much less general purpose, flexible, and adaptive than the human visual system. We argue that Deep Nets in their current form are unlikely to be able to overcome the fundamental problem of computer vision, namely how to deal with the combinatorial explosion, caused by the enormous complexity of natural images, and obtain the rich understanding of visual scenes that the human visual achieves. We argue that this combinatorial explosion takes us into a regime where “big data is not enough” and where we need to rethink our methods for benchmarking performance and evaluating vision algorithms. We stress that, as vision algorithms are increasingly used in real world applications, that performance evaluation is not merely an academic exercise but has important consequences in the real world. It is impractical to review the entire Deep Net literature so we restrict ourselves to a limited range of topics and references which are intended as entry points into the literature. The views expressed in this paper are our own and do not necessarily represent those of anybody else in the computer vision community.

In this paper we present a Bayesian framework for parsing images into their constituent visual patterns. The parsing algorithm optimizes the posterior probability and outputs a scene representation as a \"parsing graph\", in a spirit similar to parsing sentences in speech and natural language. The algorithm constructs the parsing graph and re-configures it dynamically using a set of moves, which are mostly reversible Markov chain jumps. This computational framework integrates two popular inference approaches--generative (top-down) methods and discriminative (bottom-up) methods. The former formulates the posterior probability in terms of generative models for images defined by likelihood functions and priors. The latter computes discriminative probabilities based on a sequence (cascade) of bottom-up tests/filters. In our Markov chain algorithm design, the posterior probability, defined by the generative models, is the invariant (target) probability for the Markov chain, and the discriminative probabilities are used to construct proposal probabilities to drive the Markov chain. Intuitively, the bottom-up discriminative probabilities activate top-down generative models. In this paper, we focus on two types of visual patterns--generic visual patterns, such as texture and shading, and object patterns including human faces and text. These types of patterns compete and cooperate to explain the image and so image parsing unifies image segmentation, object detection, and recognition (if we use generic visual patterns only then image parsing will correspond to image segmentation (Tu and Zhu, 2002. IEEE Trans. PAMI, 24(5):657-673). We illustrate our algorithm on natural images of complex city scenes and show examples where image segmentation can be improved by allowing object specific knowledge to disambiguate low-level segmentation cues, and conversely where object detection can be improved by using generic visual patterns to explain away shadows and occlusions.[PUBLICATION ABSTRACT]

Many man-made objects have intrinsic symmetries and often Manhattan structure. By assuming an orthographic or a weak perspective projection model, this paper addresses the estimation of 3D structures and camera projection using symmetry and/or Manhattan structure cues, for the two cases when the input is a single image or multiple images from the same category, e.g. multiple different cars from various viewpoints. More specifically, analysis on the single image case shows that Manhattan alone is sufficient to recover the camera projection and then the 3D structure can be reconstructed uniquely by exploiting symmetry. But Manhattan structure can be hard to observe from a single image due to occlusion. Hence, we extend to the multiple-image case which can also exploit symmetry but does not require Manhattan structure. We propose novel structure from motion methods for both rigid and non-rigid object deformations, which exploit symmetry and use multiple images from the same object category as input. We perform experiments on the Pascal3D+ dataset with either human labeled 2D keypoints or with 2D keypoints localized from a convolutional neural network. The results show that our methods which exploit symmetry significantly outperform the baseline methods.

Purpose In patients presenting with blunt hepatic injury (BHI), the utility of CT for triage to hepatic angiography remains uncertain since simple binary assessment of contrast extravasation (CE) as being present or absent has only modest accuracy for major arterial injury on digital subtraction angiography (DSA). American Association for the Surgery of Trauma (AAST) liver injury grading is coarse and subjective, with limited diagnostic utility in this setting. Volumetric measurements of hepatic injury burden could improve prediction. We hypothesized that in a cohort of patients that underwent catheter-directed hepatic angiography following admission trauma CT, a deep learning quantitative visualization method that calculates % liver parenchymal disruption (the LPD index, or LPDI) would add value to CE assessment for prediction of major hepatic arterial injury (MHAI). Methods This retrospective study included adult patients with BHI between 1/1/2008 and 5/1/2017 from two institutions that underwent admission trauma CT prior to hepatic angiography ( n  = 73). Presence ( n  = 41) or absence ( n  = 32) of MHAI (pseudoaneurysm, AVF, or active contrast extravasation on DSA) served as the outcome. Voxelwise measurements of liver laceration were derived using an existing multiscale deep learning algorithm trained on manually labeled data using cross-validation with a 75–25% split in four unseen folds. Liver volume was derived using a pre-trained whole liver segmentation algorithm. LPDI was automatically calculated for each patient by determining the percentage of liver involved by laceration. Classification and regression tree (CART) analyses were performed using a combination of automated LPDI measurements and either manually segmented CE volumes, or CE as a binary sign. Performance metrics for the decision rules were compared for significant differences with binary CE alone (the current standard of care for predicting MHAI), and the AAST grade. Results 36% of patients ( n  = 26) had contrast extravasation on CT. Median [Q1–Q3] automated LPDI was 4.0% [1.0–12.1%]. 41/73 (56%) of patients had MHAI. A decision tree based on auto-LPDI and volumetric CE measurements (CEvol) had the highest accuracy (0.84, 95% CI 0.73–0.91) with significant improvement over binary CE assessment (0.68, 95% CI 0.57–0.79; p  = 0.01). AAST grades at different cut-offs performed poorly for predicting MHAI, with accuracies ranging from 0.44–0.63. Decision tree analysis suggests an auto-LPDI cut-off of ≥ 12% for minimizing false negative CT exams when CE is absent or diminutive. Conclusion Current CT imaging paradigms are coarse, subjective, and limited for predicting which BHIs are most likely to benefit from AE. LPDI, automated using deep learning methods, may improve objective personalized triage of BHI patients to angiography at the point of care.

PurposeThe purpose of this study is to evaluate diagnostic performance of a commercially available radiomics research prototype vs. an in-house radiomics software in the binary classification of CT images from patients with pancreatic ductal adenocarcinoma (PDAC) vs. healthy controls.Materials and methodsIn this retrospective case–control study, 190 patients with PDAC (97 men, 93 women; 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. 3D volume of the pancreas was manually segmented from preoperative CT scans. Four hundred and seventy-eight radiomics features were extracted using in-house radiomics software. Eight hundred and fifty-four radiomics features were extracted using a commercially available research prototype. Random forest classifier was used for binary classification of PDAC vs. normal pancreas. Accuracy, sensitivity, and specificity of commercially available radiomics software were compared to in-house software.ResultsWhen 40 radiomics features were used in the random forest classification, in-house software achieved superior sensitivity (1.00) and accuracy (0.992) compared to the commercially available research prototype (sensitivity = 0.950, accuracy = 0.968). When the number of features was reduced to five features, diagnostic performance of the in-house software decreased to sensitivity (0.950), specificity (0.923), and accuracy (0.936). Diagnostic performance of the commercially available research prototype was unchanged.ConclusionCommercially available and in-house radiomics software achieve similar diagnostic performance, which may lower the barrier of entry for radiomics research and allow more clinician-scientists to perform radiomics research.

When an unknown object with Lambertian reflectance is viewed orthographically, there is an implicit ambiguity in determining its 3-d structure: we show that the object's visible surface f(x, y) is indistinguishable from a \"generalized bas-relief\" transformation of the object's geometry, ... (x, y) = λf(x, y) + μx + νy, and a corresponding transformation on the object's albedo. For each image of the object illuminated by an arbitrary number of distant light sources, there exists an identical image of the transformed object illuminated by similarly transformed light sources. This result holds both for the illuminated regions of the object as well as those in cast and attached shadows. Furthermore, neither small motion of the object, nor of the viewer will resolve the ambiguity in determining the flattening (or scaling) λ of the object's surface. Implications of this ambiguity on structure recovery and shape representation are discussed.[PUBLICATION ABSTRACT]

We propose a computational model which computes the importance of 2-D object shape parts, and we apply it to detect and localize objects with and without occlusions. The importance of a shape part (a localized contour fragment) is considered from the perspective of its contribution to the perception and recognition of the global shape of the object. Accordingly, the part importance measure is defined based on the ability to estimate/recall the global shapes of objects from the local part, namely the part’s “shape reconstructability”. More precisely, the shape reconstructability of a part is determined by two factors–part variation and part uniqueness. (i) Part variation measures the precision of the global shape reconstruction, i.e. the consistency of the reconstructed global shape with the true object shape; and (ii) part uniqueness quantifies the ambiguity of matching the part to the object, i.e. taking into account that the part could be matched to the object at several different locations. Taking both these factors into consideration, an information theoretic formulation is proposed to measure part importance by the conditional entropy of the reconstruction of the object shape from the part. Experimental results demonstrate the benefit with the proposed part importance in object detection, including the improvement of detection rate, localization accuracy, and detection efficiency. By comparing with other state-of-the-art object detectors in a challenging but common scenario, object detection with occlusions, we show a considerable improvement using the proposed importance measure, with the detection rate increased over 10 % . On a subset of the challenging PASCAL dataset, the Interpolated Average Precision (as used in the PASCAL VOC challenge) is improved by 4–8 %. Moreover, we perform a psychological experiment which provides evidence suggesting that humans use a similar measure for part importance when perceiving and recognizing shapes.