End-to-end 3D face reconstruction with deep neural networks

Monocular 3D facial shape reconstruction from a single 2D facial image has been an active research area due to its wide applications. Inspired by the success of deep neural networks (DNN), we propose a DNN-based approach for End-to-End 3D FAce Reconstruction (UH-E2FAR) from a single 2D image. Different from recent works that reconstruct and refine the 3D face in an iterative manner using both an RGB image and an initial 3D facial shape rendering, our DNN model is end-to-end, and thus the complicated 3D rendering process can be avoided. Moreover, we integrate in the DNN architecture two components, namely a multi-task loss function and a fusion convolutional neural network (CNN) to improve facial expression reconstruction. With the multi-task loss function, 3D face reconstruction is divided into neutral 3D facial shape reconstruction and expressive 3D facial shape reconstruction. The neutral 3D facial shape is class-specific. Therefore, higher layer features are useful. In comparison, the expressive 3D facial shape favors lower or intermediate layer features. With the fusion-CNN, features from different intermediate layers are fused and transformed for predicting the 3D expressive facial shape. Through extensive experiments, we demonstrate the superiority of our end-to-end framework in improving the accuracy of 3D face reconstruction.Comment: Accepted to CVPR1

Circulating tumor cells and tumor biomarkers in functional midgut neuroendocrine tumors

CALM-NET was a phase IV exploratory study in the UK that aimed to evaluate if the presence of circulating tumour cells (CTCs) at baseline predicted symptomatic response in patients with midgut neuroendocrine tumours (NETs) treated with lanreotide autogel (LAN). Adults with functional, well/moderately differentiated (Ki-67 0. Primary endpoint was the clinical value of baseline CTCs to predict symptomatic response (decrease in diarrhoea or flushing of ≥50% frequency, or ≥1 severity level). Other endpoints included progression-free survival (PFS) and correlations between plasma and urinary biomarkers (including 5-hydroxyindoleacetic acid [5-HIAA]). Fifty patients were enrolled; 40 completed the study. Baseline CTCs were present in 22 (45.8%) patients (missing baseline CTC status n = 2). Overall, 87.5% (95% confidence interval [CI]: 73.9; 94.5) of patients had a symptomatic response; a 5.9-fold higher odds of symptomatic response in patients without CTC versus patients with CTC at baseline was observed, although this was not statistically significant (odds ratio: 0.17 [95% CI: 0.02; 1.65], p = .126). One-year PFS rate was 66.4% (95% CI: 48.8; 79.2). Biomarker concentrations did not correlate to baseline CTC status. However, there was a strong correlation between plasma and urinary 5-HIAA (Spearman correlation coefficients ≥0.87 [p < .001], all time points). In conclusion, patients without CTC at baseline may be more likely to achieve a symptomatic response following LAN treatment than patients with CTC. Plasma 5-HIAA correlated with urinary 5-HIAA during LAN treatment. ClinicalTrials.gov identifier: NCT02075606

Optimal Timing of Administration of Direct-Acting Antivirals for Patients with Hepatitis C-Associated Hepatocellular Carcinoma Undergoing Liver Transplantation

Objective: To investigate the optimal timing of direct acting antiviral (DAA) administration in patients with hepatitis C-associated hepatocellular carcinoma (HCC) undergoing liver transplantation (LT). Summary of Background Data: In patients with hepatitis C (HCV) associated HCC undergoing LT, the optimal timing of direct-acting antivirals (DAA) administration to achieve sustained virologic response (SVR) and improved oncologic outcomes remains a topic of much debate. Methods: The United States HCC LT Consortium (2015–2019) was reviewed for patients with primary HCV-associated HCC who underwent LT and received DAA therapy at 20 institutions. Primary outcomes were SVR and HCC recurrence-free survival (RFS). Results: Of 857 patients, 725 were within Milan criteria. SVR was associated with improved 5-year RFS (92% vs 77%, P < 0.01). Patients who received DAAs pre-LT, 0–3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 92%, and 82%, and 5-year RFS of 93%, 94%, and 87%, respectively. Among 427 HCV treatment-naïve patients (no previous interferon therapy), patients who achieved SVR with DAAs had improved 5-year RFS (93% vs 76%, P < 0.01). Patients who received DAAs pre-LT, 0–3 months post-LT, and ≥3 months post-LT had SVR rates of 91%, 93%, and 78% (P < 0.01) and 5-year RFS of 93%, 100%, and 83% (P = 0.01). Conclusions: The optimal timing of DAA therapy appears to be 0 to 3 months after LT for HCV-associated HCC, given increased rates of SVR and improved RFS. Delayed administration after transplant should be avoided. A prospective randomized controlled trial is warranted to validate these results

3D Face Recognition for Partial Data using Semi-Coupled Dictionary Learning

Abstract-3D face recognition for partial data is a very challenging task. The task is even more challenging when the gallery sample originates from one side of the face while the probe sample originates from the other. We present a new method for computing the similarity of partial 3D data for the purpose of face recognition. This method improves upon an existing Semi-Coupled Dictionary Learning method by computing a jointly-optimized solution that incorporates the reconstruction cost, the discrimination cost and the semicoupling cost. Our experiments demonstrate that this method can improve the recognition performance of existing state-ofthe-art wavelet signatures used for 3D face recognition