Deep learning at the microscope - Working towards improved microscopy image analysis with deep neural networks

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SEMINBIO®: Innovative seeder for weed control in cereals (OK-Net Arable Practice abstract)

Trials with the SEMINBIO® seeder in southern and central Italy showed that the seeder’s sowing layout increased wheat yield, irrespective of the weed presence, and decreased weed development, if weeds were present, compared to ordinary seeders. Practical recommendation - The SEMINBIO® seeder is still at a prototype stage, but it will soon be commercially manufactured. - The SEMINBIO® seeder can be combined with the harrow weeder or any other weeding strategy to obtain an augmented weed control effect

Mortalità per sclerosi multipla nella regione Toscana

OBIETTIVI: Valutare il trend di mortalità per Sclerosi Multipla (SM) in Toscana nel ventennio 1987-2006. METODI: I dati sulla mortalità per SM sono stati ottenuti consultando gli archivi informatici delle schede di morte presso l’Agenzia Regionale di Sanità Toscana. I dati raccolti sono stati suddivisi per i quattro quinquenni compresi tra il 1987 e il 2006: (1987-1991), (1992-1996), (1997-2001), (2002-2006). Sono stati calcolati i tassi standardizzati di mortalità (metodo di standardizzazione diretta) per SM rispetto alla popolazione toscana del 2000 e i tassi di mortalità per SM specifici per sesso ed età. RISULTATI: I tassi standardizzati di mortalità con causa SM calcolati per ogni anno mostrano una diminuzione della mortalità, in entrambi i sessi, particolarmente marcata nel primo quinquennio (1987-1991). Per i maschi si osserva, nei quattro quinquenni analizzati, un trend decrescente dei tassi di mortalità che va da 0,61 per il quinquennio 1987-1991 a 0,42 per 100.000 per quello 2002-2006. Analogamente per le femmine si osserva una diminuzione da 1,29 a 0,60 per 100.000. Analizzando i tassi di mortalità per SM specifici per età abbiamo osservato un picco della mortalità nei maschi in corrispondenza della fascia di età 75-79 anni, mentre nelle donne nella fascia 65-69 anni. In entrambi i sessi il numero dei decessi aumenta fortemente dopo i 45 anni e diminuisce dopo i 79 anni. Considerando l’età di esordio, che andava dai 25 ai 35 anni, e la sopravvivenza delle persone, pari a 30-35 anni dalla diagnosi, la mortalità maggiore si riscontra nelle fasce di età da noi osservate. CONCLUSIONI: A causa della piccola dimensione del campione, tutti i valori vanno interpretati con cautela, ma i dati presenti in letteratura sono in accordo con i dati osservati nel nostro studio relativi al primo quinquennio. Infatti, il decremento di mortalità nel periodo 1987-1991 è in linea con i dati precedentemente pubblicati in uno studio sulla mortalità per SM in Italia dal 1974 al 1993 (Tassinari, 2001). Per gli anni successivi non esistono dati in letteratura relativi alla mortalità per SM in Italia. La mortalità più elevata nelle femmine riflette il maggior numero di casi di SM nella popolazione femminile ed è supportata anche dai dati presenti in letteratura sia in Italia che all’estero (Massey e Schoenberg, 1982). La mortalità per SM osservata nel nostro studio è presumibilmente una sottostima poiché talvolta l’SM non viene indicata come causa di morte

W2WNet: A two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

Ideally, Convolutional Neural Networks (CNNs) should be trained with high quality images with minimum noise and correct ground truth labels. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training and a Wiped module takes care of the final classification, while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with positive impact on the final classification accurac

Introduzione

Introduzione al numero di rivista dedicato al pensiero di Xavier Zubiri

W2WNet: a two-module probabilistic Convolutional Neural Network with embedded data cleansing functionality

Convolutional Neural Networks (CNNs) are supposed to be fed with only high-quality annotated datasets. Nonetheless, in many real-world scenarios, such high quality is very hard to obtain, and datasets may be affected by any sort of image degradation and mislabelling issues. This negatively impacts the performance of standard CNNs, both during the training and the inference phase. To address this issue we propose Wise2WipedNet (W2WNet), a new two-module Convolutional Neural Network, where a Wise module exploits Bayesian inference to identify and discard spurious images during the training, and a Wiped module takes care of the final classification while broadcasting information on the prediction confidence at inference time. The goodness of our solution is demonstrated on a number of public benchmarks addressing different image classification tasks, as well as on a real-world case study on histological image analysis. Overall, our experiments demonstrate that W2WNet is able to identify image degradation and mislabelling issues both at training and at inference time, with a positive impact on the final classification accuracy

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper, we present Generative Adversarial Network Discriminator Learner (GAN-DL), a novel self-supervised learning paradigm based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images

Exploiting generative self-supervised learning for the assessment of biological images with lack of annotations: a COVID-19 case-study

Computer-aided analysis of biological images typically requires extensive training on large-scale annotated datasets, which is not viable in many situations. In this paper we present GAN-DL, a Discriminator Learner based on the StyleGAN2 architecture, which we employ for self-supervised image representation learning in the case of fluorescent biological images. We show that Wasserstein Generative Adversarial Networks combined with linear Support Vector Machines enable high-throughput compound screening based on raw images. We demonstrate this by classifying active and inactive compounds tested for the inhibition of SARS-CoV-2 infection in VERO and HRCE cell lines. In contrast to previous methods, our deep learning based approach does not require any annotation besides the one that is normally collected during the sample preparation process. We test our technique on the RxRx19a Sars-CoV-2 image collection. The dataset consists of fluorescent images that were generated to assess the ability of regulatory-approved or in late-stage clinical trials compound to modulate the in vitro infection from SARS-CoV-2 in both VERO and HRCE cell lines. We show that our technique can be exploited not only for classification tasks, but also to effectively derive a dose response curve for the tested treatments, in a self-supervised manner. Lastly, we demonstrate its generalization capabilities by successfully addressing a zero-shot learning task, consisting in the categorization of four different cell types of the RxRx1 fluorescent images collection

Experimental and computational investigation of heat transfer in channels filled by woven spacers

Models of woven-type spacer-filled channels were investigated by Computational Fluid Dynamics (CFD) and parallel experiments in order to characterize the performance of Membrane Distillation (MD) modules. The case of overlapped spacers was analysed in a companion paper. Experiments were based on a non-intrusive technique using Thermochromic Liquid Crystals (TLC) and digital image processing, and provided the distribution of the local convective heat transfer coefficient on a thermally active wall. CFD simulations ranged from steady-state conditions to unsteady and early turbulent flow, covering a Reynolds number interval of great practical interest in real MD applications. A specific spacer aspect ratio (pitch-to-channel height ratio of 2) and two different spacer orientations with respect to the main flow (0° and 45°) were considered. Among the existing studies on spacer-filled channels, this is one of the first dealing with woven spacers, and one of the very few in which local experimental and computational heat transfer results are compared. Results suggest a convenience in adopting the 45° orientation for applications that can be operated at very low Reynolds numbers, since convenience decreases as the Reynolds number increases