Don't Forget The Past: Recurrent Depth Estimation from Monocular Video

Autonomous cars need continuously updated depth information. Thus far, depth is mostly estimated independently for a single frame at a time, even if the method starts from video input. Our method produces a time series of depth maps, which makes it an ideal candidate for online learning approaches. In particular, we put three different types of depth estimation (supervised depth prediction, self-supervised depth prediction, and self-supervised depth completion) into a common framework. We integrate the corresponding networks with a ConvLSTM such that the spatiotemporal structures of depth across frames can be exploited to yield a more accurate depth estimation. Our method is flexible. It can be applied to monocular videos only or be combined with different types of sparse depth patterns. We carefully study the architecture of the recurrent network and its training strategy. We are first to successfully exploit recurrent networks for real-time self-supervised monocular depth estimation and completion. Extensive experiments show that our recurrent method outperforms its image-based counterpart consistently and significantly in both self-supervised scenarios. It also outperforms previous depth estimation methods of the three popular groups. Please refer to https://www.trace.ethz.ch/publications/2020/rec_depth_estimation/ for details.Comment: Please refer to our webpage for details https://www.trace.ethz.ch/publications/2020/rec_depth_estimation

End-to-end Lane Detection through Differentiable Least-Squares Fitting

Lane detection is typically tackled with a two-step pipeline in which a segmentation mask of the lane markings is predicted first, and a lane line model (like a parabola or spline) is fitted to the post-processed mask next. The problem with such a two-step approach is that the parameters of the network are not optimized for the true task of interest (estimating the lane curvature parameters) but for a proxy task (segmenting the lane markings), resulting in sub-optimal performance. In this work, we propose a method to train a lane detector in an end-to-end manner, directly regressing the lane parameters. The architecture consists of two components: a deep network that predicts a segmentation-like weight map for each lane line, and a differentiable least-squares fitting module that returns for each map the parameters of the best-fitting curve in the weighted least-squares sense. These parameters can subsequently be supervised with a loss function of choice. Our method relies on the observation that it is possible to backpropagate through a least-squares fitting procedure. This leads to an end-to-end method where the features are optimized for the true task of interest: the network implicitly learns to generate features that prevent instabilities during the model fitting step, as opposed to two-step pipelines that need to handle outliers with heuristics. Additionally, the system is not just a black box but offers a degree of interpretability because the intermediately generated segmentation-like weight maps can be inspected and visualized. Code and a video is available at github.com/wvangansbeke/LaneDetection_End2End.Comment: Accepted at ICCVW 2019 (CVRSUAD-Road Scene Understanding and Autonomous Driving

SCAN: Learning to classify images without labels

status: accepte

Characterising ChIP-seq binding patterns by model-based peak shape deconvolution.

International audienceBACKGROUND: Chromatin immunoprecipitation combined with massive parallel sequencing (ChIP-seq) is widely used to study protein-chromatin interactions or chromatin modifications at genome-wide level. Sequence reads that accumulate locally at the genome (peaks) reveal loci of selectively modified chromatin or specific sites of chromatin-binding factors. Computational approaches (peak callers) have been developed to identify the global pattern of these sites, most of which assess the deviation from background by applying distribution statistics. RESULTS: We have implemented MeDiChISeq, a regression-based approach, which - by following a learning process - defines a representative binding pattern from the investigated ChIP-seq dataset. Using this model MeDiChISeq identifies significant genome-wide patterns of chromatin-bound factors or chromatin modification. MeDiChISeq has been validated for various publicly available ChIP-seq datasets and extensively compared with other peak callers. CONCLUSIONS: MeDiChI-Seq has a high resolution when identifying binding events, a high degree of peak-assessment reproducibility in biological replicates, a low level of false calls and a high true discovery rate when evaluated in the context of gold-standard benchmark datasets. Importantly, this approach can be applied not only to 'sharp' binding patterns - like those retrieved for transcription factors (TFs) - but also to the broad binding patterns seen for several histone modifications. Notably, we show that at high sequencing depths, MeDiChISeq outperforms other algorithms due to its powerful peak shape recognition capacity which facilitates discerning significant binding events from spurious background enrichment patterns that are enhanced with increased sequencing depths

Transcriptome and protein analysis highlight the endosomal pathway in disease pathogenesis of metabolic CL syndrome

C

Presentation and diagnosis of imported schistosomiasis: Relevance of eosinophilia, microscopy for ova, and serology

Background: In nonendemic countries a steady rise in cases of imported schistosomiasis has been observed. The objective of this study was to describe the presentation of patients diagnosed with schistosomiasis in the Outpatient Department (OPD) for Tropical Diseases in the Academic Medical Center, Amsterdam, the Netherlands. Methods: In a retrospective study, patients with schistosomiasis from our OPD (1997-1999), including a subgroup ol persons asking for screening for schistosomiasis and found positive, were analyzed. Diagnosis was based on freshwater exposure in an endemic area and positive serology for schistosomal antibodies. The following data were recorded: age, gender, country of birth, travel destination, symptoms, eosinophil count, and results of serology and stool and urine microscopy. Results: Seventy-eight patients (42 travelers, 16 expatriates, and 20 immigrants) were diagnosed with schisiosomiasis; 47% were infected in southern Africa. Twenty-four percent had specific symptoms, 57% had eosinophilia, and in 17 patients (22%) Schistosoma ova were found. Eleven travelers suffered from Katayama syndrome. Of the subgroup of 42 persons screened for schistosomiasis, 15 (36%) had schistosomal antibodies, the majority of these persons (10/15 [67%]) were infected in southern Africa. Conclusion: In our OPD schistosomiasis was diagnosed in about 26 patients per year, 3% of all new presentations. Infections were almost exclusively acquired in Africa. In travelers high eosinophilia was due to acute schistosomiasis; in immigants it was due to concomitant helminthic infections. One of three people asking to be screened for schistosomiasis had schistosomal antibodies. Eosinophilia was indicative but an insufficient screening tool, and stool and urine microscopy for ova were not sensitive. Screening by serology is easy and reliable and the method of choice in asymptomatic persons with a history of freshwater exposure in a high-risk are