Stima del movimento nella sintesi di immagini panoramiche in tempo reale su piattaforma DSP

La problematica centrale per la ricostruzione di immagini panoramiche da una sequenza video è la stima del movimento tra coppie di fotogrammi. Dopo un'analisi degli stimatori di moto noti in letteratura è stato proposto e caratterizzato un nuovo operatore di stima del movimento, le cui prestazioni sono state confrontate con algoritmi noti. Un algoritmo di stima del moto basato sul nuovo operatore è stato poi implementato su un sistema di elaborazione video in tempo reale: l'MVE I

Minimal-medication approaches to treating schizophrenia

UK guidelines for treating people diagnosed with schizo phrenia currently emphasise the primacy of antipsychotic medication, with or without psycho-socially based interventions as circumstances dictate. We now see increasing calls, most notably from mental health service users, for the provision of ‘whole-person-based’, minimal-medication approaches to treating people with this diagnosis. This article is intended to locate the development of such approaches within the history of modern and pre-modern psychiatry and, in doing so, summarise the available evidence base that underpins their efficacy

Quantifying the effects of data augmentation and stain color normalization in convolutional neural networks for computational pathology

Stain variation is a phenomenon observed when distinct pathology laboratories stain tissue slides that exhibit similar but not identical color appearance. Due to this color shift between laboratories, convolutional neural networks (CNNs) trained with images from one lab often underperform on unseen images from the other lab. Several techniques have been proposed to reduce the generalization error, mainly grouped into two categories: stain color augmentation and stain color normalization. The former simulates a wide variety of realistic stain variations during training, producing stain-invariant CNNs. The latter aims to match training and test color distributions in order to reduce stain variation. For the first time, we compared some of these techniques and quantified their effect on CNN classification performance using a heterogeneous dataset of hematoxylin and eosin histopathology images from 4 organs and 9 pathology laboratories. Additionally, we propose a novel unsupervised method to perform stain color normalization using a neural network. Based on our experimental results, we provide practical guidelines on how to use stain color augmentation and stain color normalization in future computational pathology applications.Comment: Accepted in the Medical Image Analysis journa

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Acromioclavicular third degree dislocation: surgical treatment in acute cases

BACKGROUND: The management of acute Rockwood type III acromioclavicular joint (ACJ) dislocation remains controversial, and the debate about whether patients should be conservatively or surgically treated continues. This study aims to compare conservative and surgical treatment of acute type III ACJ injuries in active sport participants (<35 years of age) by analysing clinical and radiological results after a minimum of 24 months follow-up. METHODS: The records of 72 patients with acute type III ACJ dislocations who were treated from January 2006 to December 2011 were retrospectively evaluated. Patients were categorised into two groups. group A included 25 patients treated conservatively, and group B included 30 patients treated surgically with the TightRope™ system. Seventeen patients were lost to follow-up. All patients were evaluated at final follow-up with these clinical scores: Constant, University of California Los Angeles scale (UCLA), American Shoulder and Elbow Surgeons Scale (ASES) and Acromioclavicular Joint Instability (ACJI) and with a subjective evaluation of the patient satisfaction, aesthetic results and shoulder function. The distance between the acromion and clavicle and between the coracoid process and clavicle were evaluated radiographically and compared with preoperative values. Δ, the difference in mm between the distance at the final follow-up and at T0 in the injured shoulder, and α, the side-to-side difference in mm at follow-up, were calculated. Heterotopic ossification and postoperative osteolysis were evaluated in both groups. RESULTS: There were no major intraoperative complications in the surgical group. The subjective parameters significantly differed between the two groups. Constant, ASES and UCLA scores were similar in both groups (P > 0.05), whereas ACJI results favoured the surgical group (group A, 72.4; group B, 87.9; P < 0.05). All measurements of radiographic evaluation were significantly reduced in the surgical group compared with the conservative group. In group A, we detected calcifications in 30% of patients; in group B we detected two cases of moderate osteolysis and calcifications in 70% of patients. CONCLUSION: Although better subjective and radiographic results were achieved in surgically treated patients, traditional objective scores did not show significant differences between the two groups. Our results cannot support routine use of surgery to treat type III ACJ dislocations

Towards automatic pulmonary nodule management in lung cancer screening with deep learning

The introduction of lung cancer screening programs will produce an unprecedented amount of chest CT scans in the near future, which radiologists will have to read in order to decide on a patient follow-up strategy. According to the current guidelines, the workup of screen-detected nodules strongly relies on nodule size and nodule type. In this paper, we present a deep learning system based on multi-stream multi-scale convolutional networks, which automatically classifies all nodule types relevant for nodule workup. The system processes raw CT data containing a nodule without the need for any additional information such as nodule segmentation or nodule size and learns a representation of 3D data by analyzing an arbitrary number of 2D views of a given nodule. The deep learning system was trained with data from the Italian MILD screening trial and validated on an independent set of data from the Danish DLCST screening trial. We analyze the advantage of processing nodules at multiple scales with a multi-stream convolutional network architecture, and we show that the proposed deep learning system achieves performance at classifying nodule type that surpasses the one of classical machine learning approaches and is within the inter-observer variability among four experienced human observers.Comment: Published on Scientific Report

Relation between plaque type, plaque thickness, blood shear stress, and plaque stress in coronary arteries assessed by X-ray Angiography and Intravascular Ultrasound

Purpose: Atheromatic plaque progression is affected, among others phenomena, by biomechanical, biochemical, and physiological factors. In this paper, the authors introduce a novel framework able to provide both morphological (vessel radius, plaque thickness, and type) and biomechanical (wall shear stress and Von Mises stress) indices of coronary arteries. Methods: First, the approach reconstructs the three-dimensional morphology of the vessel from intravascular ultrasound(IVUS) and Angiographic sequences, requiring minimal user interaction. Then, a computational pipeline allows to automatically assess fluid-dynamic and mechanical indices. Ten coronary arteries are analyzed illustrating the capabilities of the tool and confirming previous technical and clinical observations. Results: The relations between the arterial indices obtained by IVUS measurement and simulations have been quantitatively analyzed along the whole surface of the artery, extending the analysis of the coronary arteries shown in previous state of the art studies. Additionally, for the first time in the literature, the framework allows the computation of the membrane stresses using a simplified mechanical model of the arterial wall. Conclusions: Circumferentially (within a given frame), statistical analysis shows an inverse relation between the wall shear stress and the plaque thickness. At the global level (comparing a frame within the entire vessel), it is observed that heavy plaque accumulations are in general calcified and are located in the areas of the vessel having high wall shear stress. Finally, in their experiments the inverse proportionality between fluid and structural stresses is observed

Deep Learning to Analyze RNA-Seq Gene Expression Data

Deep learning models are currently being applied in several areas with great success. However, their application for the analysis of high-throughput sequencing data remains a challenge for the research community due to the fact that this family of models are known to work very well in big datasets with lots of samples available, just the opposite scenario typically found in biomedical areas. In this work, a first approximation on the use of deep learning for the analysis of RNA-Seq gene expression profiles data is provided. Three public cancer-related databases are analyzed using a regularized linear model (standard LASSO) as baseline model, and two deep learning models that differ on the feature selection technique used prior to the application of a deep neural net model. The results indicate that a straightforward application of deep nets implementations available in public scientific tools and under the conditions described within this work is not enough to outperform simpler models like LASSO. Therefore, smarter and more complex ways that incorporate prior biological knowledge into the estimation procedure of deep learning models may be necessary in order to obtain better results in terms of predictive performance.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

«The Future of Humans in a Post-Human World»: Frankissstein by Jeanette Winterson

&nbsp;Frankissstein: A Love Story, Jeanette Winterson's 2019 novel, is a mirror transposition of Mary Shelley’s Frankenstein. The novel adumbrates a posthuman or transhuman life to be lived “forever as brain emulation” (Winterson 2020, 104). What was traditionally known as the human being is now required to transcend biology through “better biology” (ibid. 113), i.e., Artificial Intelligence.&nbsp; Assuming that homo sapiens is not a special case, an ontology that transcends the human/nonhuman divide is proposed in Winterson’s story by scientist Victor Stein. He assumes that we can develop our brain software through learning, including outsourcing to machines, until we learn to share the planet with “non biological forms created by us” (ibid. 73). This delineates a utopian dimension in which the relationship between self, other and power is reworked so that as, in Donna Haraway’s words, there is “agency ...without defended subjects” (Haraway 1991, 3). Or, in other words, a world in which the cyborgification (the fusion of nature and culture/technology) is seen as inevitable and there is no need to ‘defend’ nature. With further lines of thought, my paper explores the metaphorical fields (parallel worlds, simulacra) and narrative devices (metalepsis, alternating montage, internal parallelism) that underpin this story. My point is that the attempted fusion of nature and technology, as theorised by techno-scientists in Winterson’s story, only produces a modification in the attitude of some unaugmented humans towards other unaugmented humans, both living and dead. Eventually humans are not cyborgs, nor inforgs, nor full-blown transhumans but boundary creatures straddling alternative ontologies and often acting as less than humans, infrahumans or, like transexual Ry Shelley, “inappropriate others” (Haraway 1992).Frankissstein: A Love Story, romanzo di Jeanette Winterson pubblicato nel 2019, è una ‘trasposizione a specchio’ del Frankenstein di Mary Shelley. Il testo adombra una vita post-umana o transumana da vivere “forever as brain emulation” (Winterson 2020, 104). Ciò che si è comunemente sempre definito ‘essere umano’ deve ora trascendere la biologia attraverso una "better biology" (ibid. 113), ovvero mediante l'intelligenza artificiale.&nbsp; Assumendo che l'homo sapiens non è un caso speciale, lo scienziato Victor Stein propone, nel racconto di Winterson, un'ontologia che trascende la divisione umano/non umano. Egli ipotizza che possiamo sviluppare il nostro software cerebrale attraverso l'apprendimento e l'esternalizzazione cognitiva a vantaggio delle macchine, fino ad imparare a condividere il pianeta con " non biological forms created by us " (ibid. 73). Entro tale dimensione utopica il rapporto fra il sé, l'altro e il potere è rielaborato in modo che, come scrive Donna Haraway, vi sia " agency ...without defended subjects " (Haraway 1991, 3). O, in altre parole, un mondo in cui la cyborgificazione (la fusione di natura e cultura/tecnologia) sia vista come inevitabile e non vi sia più necessità di 'difendere' la natura. Oltre a investigare queste prospettive di pensiero, il presente articolo esplora i principali campi metaforici del racconto (mondi paralleli, simulacri) e i suoi dispositivi narrativi essenziali (metalepsi, montaggio alternato, parallelismo interno). Lo studio argomenta che il tentativo di fusione tra natura e tecnologia, come teorizzato dai tecno-scienziati nel racconto di Winterson, al più modifica l'atteggiamento di alcuni esseri umani ‘non aumentati’ nei confronti di altri esseri umani ‘non aumentati’, siano essi viventi o morti. Gli umani non sono cyborg, né inforgs, né transumani ad alcun titolo, ma creature di confine tra ontologie alternative, che spesso agiscono da ‘meno che umani’, infraumani o, come il transessuale Ry Shelley, " inappropriate others " (Haraway 1992). &nbsp