U-Net: Convolutional Networks for Biomedical Image Segmentation

There is large consent that successful training of deep networks requires many thousand annotated training samples. In this paper, we present a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently. The architecture consists of a contracting path to capture context and a symmetric expanding path that enables precise localization. We show that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Using the same network trained on transmitted light microscopy images (phase contrast and DIC) we won the ISBI cell tracking challenge 2015 in these categories by a large margin. Moreover, the network is fast. Segmentation of a 512x512 image takes less than a second on a recent GPU. The full implementation (based on Caffe) and the trained networks are available at http://lmb.informatik.uni-freiburg.de/people/ronneber/u-net .Comment: conditionally accepted at MICCAI 201

A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery

We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.Comment: 10 pages, 8 figures. ACM SIGSPATIAL 2018, Seattle, US

Separating Reflection and Transmission Images in the Wild

The reflections caused by common semi-reflectors, such as glass windows, can impact the performance of computer vision algorithms. State-of-the-art methods can remove reflections on synthetic data and in controlled scenarios. However, they are based on strong assumptions and do not generalize well to real-world images. Contrary to a common misconception, real-world images are challenging even when polarization information is used. We present a deep learning approach to separate the reflected and the transmitted components of the recorded irradiance, which explicitly uses the polarization properties of light. To train it, we introduce an accurate synthetic data generation pipeline, which simulates realistic reflections, including those generated by curved and non-ideal surfaces, non-static scenes, and high-dynamic-range scenes.Comment: accepted at ECCV 201

Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network

Depth estimation from a single image is a fundamental problem in computer vision. In this paper, we propose a simple yet effective convolutional spatial propagation network (CSPN) to learn the affinity matrix for depth prediction. Specifically, we adopt an efficient linear propagation model, where the propagation is performed with a manner of recurrent convolutional operation, and the affinity among neighboring pixels is learned through a deep convolutional neural network (CNN). We apply the designed CSPN to two depth estimation tasks given a single image: (1) To refine the depth output from state-of-the-art (SOTA) existing methods; and (2) to convert sparse depth samples to a dense depth map by embedding the depth samples within the propagation procedure. The second task is inspired by the availability of LIDARs that provides sparse but accurate depth measurements. We experimented the proposed CSPN over two popular benchmarks for depth estimation, i.e. NYU v2 and KITTI, where we show that our proposed approach improves in not only quality (e.g., 30% more reduction in depth error), but also speed (e.g., 2 to 5 times faster) than prior SOTA methods.Comment: 14 pages, 8 figures, ECCV 201

Deep Learning for ECG Segmentation

We propose an algorithm for electrocardiogram (ECG) segmentation using a UNet-like full-convolutional neural network. The algorithm receives an arbitrary sampling rate ECG signal as an input, and gives a list of onsets and offsets of P and T waves and QRS complexes as output. Our method of segmentation differs from others in speed, a small number of parameters and a good generalization: it is adaptive to different sampling rates and it is generalized to various types of ECG monitors. The proposed approach is superior to other state-of-the-art segmentation methods in terms of quality. In particular, F1-measures for detection of onsets and offsets of P and T waves and for QRS-complexes are at least 97.8%, 99.5%, and 99.9%, respectively.Comment: 10 pages, 7 figure

Improving Lesion Segmentation for Diabetic Retinopathy using Adversarial Learning

Diabetic Retinopathy (DR) is a leading cause of blindness in working age adults. DR lesions can be challenging to identify in fundus images, and automatic DR detection systems can offer strong clinical value. Of the publicly available labeled datasets for DR, the Indian Diabetic Retinopathy Image Dataset (IDRiD) presents retinal fundus images with pixel-level annotations of four distinct lesions: microaneurysms, hemorrhages, soft exudates and hard exudates. We utilize the HEDNet edge detector to solve a semantic segmentation task on this dataset, and then propose an end-to-end system for pixel-level segmentation of DR lesions by incorporating HEDNet into a Conditional Generative Adversarial Network (cGAN). We design a loss function that adds adversarial loss to segmentation loss. Our experiments show that the addition of the adversarial loss improves the lesion segmentation performance over the baseline.Comment: Accepted to International Conference on Image Analysis and Recognition, ICIAR 2019. Published at https://doi.org/10.1007/978-3-030-27272-2_29 Code: https://github.com/zoujx96/DR-segmentatio

ANALISA LAPORAN KEUANGAN SEBAGAI DASAR PENGUKURAN TINGKAT PROFITABILITAS PADA PERUSAHAAN DAERAH AIR MINUM (PDAM) KABUPATEN KARAWANG PERIODE 2002-2004

Perusahaan Daerah Air Minum (PDAM) Kabupaten Karawang adalah perusahaan yang bergerak dalam bidang jasa penyediaan air minum yang didistribusikan kepada masyarakat. Berdasarkan laporan keuangan PDAM Kabupaten Karawang yang diambil dalam penelitian pada periode 2002 hingga periode 2004 menggambarkan adanya fluktuasi pada salah satu rasio profitabilitasnya, yaitu pada rasio Basic Earning Power. Adapun tujuan yang ingin dicapai dalam penelitian ini adalah untuk mengetahui kondisi keuangan perusahaan, mengetahui analisa laporan keuangan sebagai dasar pengukuran profitabilitas perusahaan serta mengetahui faktor-faktor yang mempengaruhi laporan keuangan terhadap profitabilitas perusahaan. Metode penelitian yang digunakan adalah metode penelitian deskriptif dan teknik pengumpulan data yang digunakan adalah studi kepustakaan dan studi lapangan yang terdiri dari wawancara terstruktur dan observasi non partisipan. Penelitian tersebut dilakukan dengan menggunakan metode analisis data horizontal, sedangkan teknik analisis data yang dipergunakan adalah dengan teknik analisis trend dan analisis rasio profitabilitas. PDAM Kabupaten Karawang didalam perkembangan tingkat profitabilitasnya mengalami fluktuasi, terutama pada rasio basic earning powernya. Tingkat fluktuasi atas rasio profitabilitas perusahaan tersebut dipengaruhi oleh kenaikan tarif dasar air per m3 sebesar 40% hingga akhir periode 2004, selain itu didukung pula dengan peningkatan frekuensi sambungan rekening baru dengan mayoritas konsumen dari golongan niaga. Berdasarkan uraian diatas, maka hasil analisa laporan keuangan dapat dijadikan sebagai dasar pengukuran tingkat profitabilitas perusahaan. Faktor-faktor yang mempengaruhi laporan keuangan terhadap tingkat profitabilitas pada PDAM Karawang yaitu tingginya biaya pengolahan air melebihi biaya pembelian air baku, lemahnya kinerja bagian Satuan Pengendalian Internal sehingga memudahkan terjadinya penyimpangan disipliner serta sering terjadinya keterlambatan penyampaian laporan keuangan cabang sehingga penyusunan laporan pusat seringkali tidak tetap dan cenderung kadaluarsa. Saran yang peneliti kemukakan yaitu PDAM Karawang seharusnya menerapkan metode komputerisasi yang terkoneksi pada seluruh kantor cabang serta kerjasama dengan pihak luar (bank), guna memperkecil penyimpangan, mempermudah koordinasi serta memudahkan proses penyampaian data laporan keuangan setiap cabang sehingga diharapkan tidak akan terjadi lagi keterlambatan, yang kedua meningkatkan kinerja Satuan Pengendalian Internal agar mampu mengurangi bentuk penyimpangan