- a large-scale dataset of 3D medical shapes for computer vision.

Objectives: The shape is commonly used to describe the objects. State-of-the-art algorithms in medical imaging are predominantly diverging from computer vision, where voxel grids, meshes, point clouds, and implicit surface models are used. This is seen from the growing popularity of ShapeNet (51,300 models) and Princeton ModelNet (127,915 models).

OOD-CV-v2 : An Extended Benchmark for Robustness to Out-of-Distribution Shifts of Individual Nuisances in Natural Images.

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms of pose, shape, texture, context and the weather conditions, and enables benchmarking of models for image classification, object detection, and 3D pose estimation.

Large-scale pancreatic cancer detection via non-contrast CT and deep learning.

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.

Two Computational Approaches to Visual Analogy: Task-Specific Models Versus Domain-General Mapping.

Advances in artificial intelligence have raised a basic question about human intelligence: Is human reasoning best emulated by applying task-specific knowledge acquired from a wealth of prior experience, or is it based on the domain-general manipulation and comparison of mental representations? We address this question for the case of visual analogical reasoning. Using realistic images of familiar three-dimensional objects (cars and their parts), we systematically manipulated viewpoints, part relations, and entity properties in visual analogy problems. We compared human performance to that of two recent deep learning models (Siamese Network and Relation Network) that were directly trained to solve these problems and to apply their task-specific knowledge to analogical reasoning.

External Attention Assisted Multi-Phase Splenic Vascular Injury Segmentation With Limited Data.

The spleen is one of the most commonly injured solid organs in blunt abdominal trauma. The development of automatic segmentation systems from multi-phase CT for splenic vascular injury can augment severity grading for improving clinical decision support and outcome prediction. However, accurate segmentation of splenic vascular injury is challenging for the following reasons: 1) Splenic vascular injury can be highly variant in shape, texture, size, and overall appearance; and 2) Data acquisition is a complex and expensive procedure that requires intensive efforts from both data scientists and radiologists, which makes large-scale well-annotated datasets hard to acquire in general.

CT Radiomics-Based Preoperative Survival Prediction in Patients With Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is often a lethal malignancy with limited preoperative predictors of long-term survival. The purpose of this study was to evaluate the prognostic utility of preoperative CT radiomics features in predicting postoperative survival of patients with PDAC. A total of 153 patients with surgically resected PDAC who underwent preoperative CT between 2011 and 2017 were retrospectively identified.

Current Status of Radiomics and Deep Learning in Liver Imaging.

Artificial intelligence is poised to revolutionize medical image. It takes advantage of the high-dimensional quantitative features present in medical images that may not be fully appreciated by humans. Artificial intelligence has the potential to facilitate automatic organ segmentation, disease detection and characterization, and prediction of disease recurrence.

Learning Inductive Attention Guidance for Partially Supervised Pancreatic Ductal Adenocarcinoma Prediction.

Pancreatic ductal adenocarcinoma (PDAC) is the third most common cause of cancer death in the United States. Predicting tumors like PDACs (including both classification and segmentation) from medical images by deep learning is becoming a growing trend, but usually a large number of annotated data are required for training, which is very labor-intensive and time-consuming. In this paper, we consider a partially supervised setting, where cheap image-level annotations are provided for all the training data, and the costly per-voxel annotations are only available for a subset of them.

Added value of deep learning-based liver parenchymal CT volumetry for predicting major arterial injury after blunt hepatic trauma: a decision tree analysis.

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.

A Multiscale Deep Learning Method for Quantitative Visualization of Traumatic Hemoperitoneum at CT: Assessment of Feasibility and Comparison with Subjective Categorical Estimation.

Purpose: To evaluate the feasibility of a multiscale deep learning algorithm for quantitative visualization and measurement of traumatic hemoperitoneum and to compare diagnostic performance for relevant outcomes with categorical estimation.

Materials And Methods: This retrospective, single-institution study included 130 patients (mean age, 38 years; interquartile range, 25-50 years; 79 men) with traumatic hemoperitoneum who underwent CT of the abdomen and pelvis at trauma admission between January 2016 and April 2019. Labeled cases were separated into five combinations of training (80%) and test (20%) sets, and fivefold cross-validation was performed.

Pancreatic Cancer Imaging: A New Look at an Old Problem.

Computed tomography is the most commonly used imaging modality to detect and stage pancreatic cancer. Previous advances in pancreatic cancer imaging have focused on optimizing image acquisition parameters and reporting standards. However, current state-of-the-art imaging approaches still misdiagnose some potentially curable pancreatic cancers and do not provide prognostic information or inform optimal management strategies beyond stage.

Diagnostic performance of commercially available vs. in-house radiomics software in classification of CT images from patients with pancreatic ductal adenocarcinoma vs. healthy controls.

Purpose: The 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.

Deep learning-based quantitative visualization and measurement of extraperitoneal hematoma volumes in patients with pelvic fractures: Potential role in personalized forecasting and decision support.

Introduction: Admission computed tomography (CT) is a widely used diagnostic tool for patients with pelvic fractures. In this pilot study, we hypothesized that pelvic hematoma volumes derived using a rapid automated deep learning-based quantitative visualization and measurement algorithm predict interventions and outcomes including (a) need for angioembolization (AE), pelvic packing (PP), or massive transfusion (MT), and (b) in-hospital mortality.

Methods: We performed a single-institution retrospective analysis of 253 patients with bleeding pelvic fractures who underwent admission abdominopelvic trauma CT between 2008 and 2017.

Recurrent Saliency Transformation Network for Tiny Target Segmentation in Abdominal CT Scans.

We aim at segmenting a wide variety of organs, including tiny targets (e.g., adrenal gland), and neoplasms (e.

Performance of a Deep Learning Algorithm for Automated Segmentation and Quantification of Traumatic Pelvic Hematomas on CT.

The volume of pelvic hematoma at CT has been shown to be the strongest independent predictor of major arterial injury requiring angioembolization in trauma victims with pelvic fractures, and also correlates with transfusion requirement and mortality. Measurement of pelvic hematomas (unopacified extraperitoneal blood accumulated from time of injury) using semi-automated seeded region growing is time-consuming and requires trained experts, precluding routine measurement at the point of care. Pelvic hematomas are markedly variable in shape and location, have irregular ill-defined margins, have low contrast with respect to viscera and muscle, and reside within anatomically distorted pelvises.

Abdominal multi-organ segmentation with organ-attention networks and statistical fusion.

Accurate and robust segmentation of abdominal organs on CT is essential for many clinical applications such as computer-aided diagnosis and computer-aided surgery. But this task is challenging due to the weak boundaries of organs, the complexity of the background, and the variable sizes of different organs. To address these challenges, we introduce a novel framework for multi-organ segmentation of abdominal regions by using organ-attention networks with reverse connections (OAN-RCs) which are applied to 2D views, of the 3D CT volume, and output estimates which are combined by statistical fusion exploiting structural similarity.

Utility of CT Radiomics Features in Differentiation of Pancreatic Ductal Adenocarcinoma From Normal Pancreatic Tissue.

The objective of our study was to determine the utility of radiomics features in differentiating CT cases of pancreatic ductal adenocarcinoma (PDAC) from normal pancreas. In this retrospective case-control study, 190 patients with PDAC (97 men, 93 women; mean age ± SD, 66 ± 9 years) from 2012 to 2017 and 190 healthy potential renal donors (96 men, 94 women; mean age ± SD, 52 ± 8 years) without known pancreatic disease from 2005 to 2009 were identified from radiology and pathology databases. The 3D volume of the pancreas was manually segmented from the preoperative CT scans by four trained researchers and verified by three abdominal radiologists.

Robust 3D Human Pose Estimation from Single Images or Video Sequences.

We propose a method for estimating 3D human poses from single images or video sequences. The task is challenging because: (a) many 3D poses can have similar 2D pose projections which makes the lifting ambiguous, and (b) current 2D joint detectors are not accurate which can cause big errors in 3D estimates. We represent 3D poses by a sparse combination of bases which encode structural pose priors to reduce the lifting ambiguity.

DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs.

In this work we address the task of semantic image segmentation with Deep Learning and make three main contributions that are experimentally shown to have substantial practical merit. First, we highlight convolution with upsampled filters, or 'atrous convolution', as a powerful tool in dense prediction tasks. Atrous convolution allows us to explicitly control the resolution at which feature responses are computed within Deep Convolutional Neural Networks.

Semi-Supervised Sparse Representation Based Classification for Face Recognition With Insufficient Labeled Samples.

This paper addresses the problem of face recognition when there is only few, or even only a single, labeled examples of the face that we wish to recognize. Moreover, these examples are typically corrupted by nuisance variables, both linear (i.e.

Empirical Minimum Bayes Risk Prediction.

When building vision systems that predict structured objects such as image segmentations or human poses, a crucial concern is performance under task-specific evaluation measures (e.g., Jaccard Index or Average Precision).

Non-Rigid Point Set Registration by Preserving Global and Local Structures.

In previous work on point registration, the input point sets are often represented using Gaussian mixture models and the registration is then addressed through a probabilistic approach, which aims to exploit global relationships on the point sets. For non-rigid shapes, however, the local structures among neighboring points are also strong and stable and thus helpful in recovering the point correspondence. In this paper, we formulate point registration as the estimation of a mixture of densities, where local features, such as shape context, are used to assign the membership probabilities of the mixture model.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking.

An Active Patch Model for Real World Texture and Appearance Classification.

This paper addresses the task of natural texture and appearance classification. Our goal is to develop a simple and intuitive method that performs at state of the art on datasets ranging from homogeneous texture (e.g.