Using of geophysical methods for estimation of the technological properties of ores of pyrite-polymetallic deposits

The complex of well and logging methods providing efficient estimation of the technological properties of the main ore minerals and ores in process of deposits prospecting is developed basing on study of their petrophysical properties. It provides selection of representative technological probes and development on this basis of optimum methods of processing of ores. Formation of physical-geological technological model of the deposit will allow to reduce losses of metals on concentrating repartition

Recent advances in anion–π interactions

Over the past 10 years, anion–π interaction has been recognized as an important weak force that may occur between anionic systems and electron- deficient aromatics. Lately, this supramolecular contact has experienced a rapidly growing interest, as reflected by numerous recent literature reports. The present paper highlights the tremendous progress achieved in the field by emphasizing three important studies involving anion–π interactions published in 2010. In addition, a pioneering search of the Protein Data Bank (PDB) reveals short anion–π contacts in some protein structures

Fully Unsupervised Probabilistic Noise2Void

Image denoising is the first step in many biomedical image analysis pipelines and Deep Learning (DL) based methods are currently best performing. A new category of DL methods such as Noise2Void or Noise2Self can be used fully unsupervised, requiring nothing but the noisy data. However, this comes at the price of reduced reconstruction quality. The recently proposed Probabilistic Noise2Void (PN2V) improves results, but requires an additional noise model for which calibration data needs to be acquired. Here, we present improvements to PN2V that (i) replace histogram based noise models by parametric noise models, and (ii) show how suitable noise models can be created even in the absence of calibration data. This is a major step since it actually renders PN2V fully unsupervised. We demonstrate that all proposed improvements are not only academic but indeed relevant.Comment: Accepted at ISBI 202

{\mu}Split: efficient image decomposition for microscopy data

We present {\mu}Split, a dedicated approach for trained image decomposition in the context of fluorescence microscopy images. We find that best results using regular deep architectures are achieved when large image patches are used during training, making memory consumption the limiting factor to further improving performance. We therefore introduce lateral contextualization (LC), a memory efficient way to train powerful networks and show that LC leads to consistent and significant improvements on the task at hand. We integrate LC with U-Nets, Hierarchical AEs, and Hierarchical VAEs, for which we formulate a modified ELBO loss. Additionally, LC enables training deeper hierarchical models than otherwise possible and, interestingly, helps to reduce tiling artefacts that are inherently impossible to avoid when using tiled VAE predictions. We apply {\mu}Split to five decomposition tasks, one on a synthetic dataset, four others derived from real microscopy data. LC achieves SOTA results (average improvements to the best baseline of 2.36 dB PSNR), while simultaneously requiring considerably less GPU memory.Comment: Published at ICCV 2023. 10 pages, 7 figures, 9 pages supplement, 8 supplementary figure

DenoiSeg: Joint Denoising and Segmentation

Microscopy image analysis often requires the segmentation of objects, but training data for this task is typically scarce and hard to obtain. Here we propose DenoiSeg, a new method that can be trained end-to-end on only a few annotated ground truth segmentations. We achieve this by extending Noise2Void, a self-supervised denoising scheme that can be trained on noisy images alone, to also predict dense 3-class segmentations. The reason for the success of our method is that segmentation can profit from denoising, especially when performed jointly within the same network. The network becomes a denoising expert by seeing all available raw data, while co-learning to segment, even if only a few segmentation labels are available. This hypothesis is additionally fueled by our observation that the best segmentation results on high quality (very low noise) raw data are obtained when moderate amounts of synthetic noise are added. This renders the denoising-task non-trivial and unleashes the desired co-learning effect. We believe that DenoiSeg offers a viable way to circumvent the tremendous hunger for high quality training data and effectively enables few-shot learning of dense segmentations.Comment: 10 pages, 4 figures, 2 pages supplement (4 figures

Leveraging Self-supervised Denoising for Image Segmentation

Deep learning (DL) has arguably emerged as the method of choice for the detection and segmentation of biological structures in microscopy images. However, DL typically needs copious amounts of annotated training data that is for biomedical projects typically not available and excessively expensive to generate. Additionally, tasks become harder in the presence of noise, requiring even more high-quality training data. Hence, we propose to use denoising networks to improve the performance of other DL-based image segmentation methods. More specifically, we present ideas on how state-of-the-art self-supervised CARE networks can improve cell/nuclei segmentation in microscopy data. Using two state-of-the-art baseline methods, U-Net and StarDist, we show that our ideas consistently improve the quality of resulting segmentations, especially when only limited training data for noisy micrographs are available.Comment: accepted at ISBI 202

Probabilistic Noise2Void: Unsupervised Content-Aware Denoising

Today, Convolutional Neural Networks (CNNs) are the leading method for image denoising. They are traditionally trained on pairs of images, which are often hard to obtain for practical applications. This motivates self-supervised training methods, such as Noise2Void (N2V) that operate on single noisy images. Self-supervised methods are, unfortunately, not competitive with models trained on image pairs. Here, we present Probabilistic Noise2Void (PN2V), a method to train CNNs to predict per-pixel intensity distributions. Combining these with a suitable description of the noise, we obtain a complete probabilistic model for the noisy observations and true signal in every pixel. We evaluate PN2V on publicly available microscopy datasets, under a broad range of noise regimes, and achieve competitive results with respect to supervised state-of-the-art methods