PDL: Regularizing Multiple Instance Learning with Progressive Dropout Layers

Multiple instance learning (MIL) was a weakly supervised learning approach that sought to assign binary class labels to collections of instances known as bags. However, due to their weak supervision nature, the MIL methods were susceptible to overfitting and required assistance in developing comprehensive representations of target instances. While regularization typically effectively combated overfitting, its integration with the MIL model has been frequently overlooked in prior studies. Meanwhile, current regularization methods for MIL have shown limitations in their capacity to uncover a diverse array of representations. In this study, we delve into the realm of regularization within the MIL model, presenting a novel approach in the form of a Progressive Dropout Layer (PDL). We aim to not only address overfitting but also empower the MIL model in uncovering intricate and impactful feature representations. The proposed method was orthogonal to existing MIL methods and could be easily integrated into them to boost performance. Our extensive evaluation across a range of MIL benchmark datasets demonstrated that the incorporation of the PDL into multiple MIL methods not only elevated their classification performance but also augmented their potential for weakly-supervised feature localizations.Comment: The code is available in https://github.com/ChongQingNoSubway/PD

NNMobile-Net: Rethinking CNN Design for Deep Learning-Based Retinopathy Research

Retinal diseases (RD) are the leading cause of severe vision loss or blindness. Deep learning-based automated tools play an indispensable role in assisting clinicians in diagnosing and monitoring RD in modern medicine. Recently, an increasing number of works in this field have taken advantage of Vision Transformer to achieve state-of-the-art performance with more parameters and higher model complexity compared to Convolutional Neural Networks (CNNs). Such sophisticated and task-specific model designs, however, are prone to be overfitting and hinder their generalizability. In this work, we argue that a channel-aware and well-calibrated CNN model may overcome these problems. To this end, we empirically studied CNN's macro and micro designs and its training strategies. Based on the investigation, we proposed a no-new-MobleNet (nn-MobileNet) developed for retinal diseases. In our experiments, our generic, simple and efficient model superseded most current state-of-the-art methods on four public datasets for multiple tasks, including diabetic retinopathy grading, fundus multi-disease detection, and diabetic macular edema classification. Our work may provide novel insights into deep learning architecture design and advance retinopathy research.Comment: Code will publish soon: https://github.com/Retinal-Research/NNMOBILE-NE

OTRE: Where Optimal Transport Guided Unpaired Image-to-Image Translation Meets Regularization by Enhancing

Non-mydriatic retinal color fundus photography (CFP) is widely available due to the advantage of not requiring pupillary dilation, however, is prone to poor quality due to operators, systemic imperfections, or patient-related causes. Optimal retinal image quality is mandated for accurate medical diagnoses and automated analyses. Herein, we leveraged the Optimal Transport (OT) theory to propose an unpaired image-to-image translation scheme for mapping low-quality retinal CFPs to high-quality counterparts. Furthermore, to improve the flexibility, robustness, and applicability of our image enhancement pipeline in the clinical practice, we generalized a state-of-the-art model-based image reconstruction method, regularization by denoising, by plugging in priors learned by our OT-guided image-to-image translation network. We named it as regularization by enhancing (RE). We validated the integrated framework, OTRE, on three publicly available retinal image datasets by assessing the quality after enhancement and their performance on various downstream tasks, including diabetic retinopathy grading, vessel segmentation, and diabetic lesion segmentation. The experimental results demonstrated the superiority of our proposed framework over some state-of-the-art unsupervised competitors and a state-of-the-art supervised method.Comment: Accepted as a conference paper to The 28th biennial international conference on Information Processing in Medical Imaging (IPMI 2023

Dependable workflow management system for smart farms

Smart Farming is a new and emerging domain representing the application of modern technologies into agriculture, leading to a revolution of this classic domain. CLUeFARM is a web platform in the domain of smart farming which main purpose is to help farmers to easily manage and supervise their farms from any device connected to the Internet, offering some useful services. Cloud technologies evolved a lot in recent years and based on this growth, microservices are more and more used. If for the server side, the scalability and reusability are solved in high proportion by microservices, on the client side of web applications, there was no independent solution until the recent emergence of web components. They can be seen as the microservices of the front-end. Microservices and web components are usually used isolated one of each other. This paper proposes and presents the functionality and implementation of a dependable workflow management service by using an end-to-end microservices approach

Collapse of an Instanton

We construct a two parameter family of collapsing solutions to the 4+1 Yang-Mills equations and derive the dynamical law of the collapse. Our arguments indicate that this family of solutions is stable. The latter fact is also supported by numerical simulations.Comment: 17 pages, 1 figur

The attitude of patients with progressive ataxias towards clinical trials

Background The development of new therapies may rely on the conduct of human experimentation as well as later clinical trials of therapeutic interventions. Ethical considerations seek to protect the patient from risk but few have sought to ascertain the attitude to such risk of patients with progressive debilitating or terminal conditions, for which no mitigating or curative therapies exist. Such understanding is also important if recruitment is to be maximized. We therefore sought to define the motivations for and barriers to trial participation amongst patients with progressive ataxias, as well as their condition-specific trial preferences. Methods We conducted an online survey consisting of 29 questions covering four key domains (demographics, personal motivation, drug therapy and study design) relating to the design of clinical trials. Two major ataxia charities, Ataxia UK and the Friedreich’s Ataxia Research Alliance (FARA) sent the survey to their members. Responses were analysed by disease and by ambulatory status. Results Of 342 respondents, 204 reported a diagnosis of Friedreich’s ataxia (FRDA), 55 inherited cerebellar ataxia (CA) and 70 idiopathic CA. The most important symptoms to be addressed by a trial were considered to be balance problems and ambulation, although these were superseded by speech problems in wheelchair users. Common motivations for participation were potential benefits to self and others. Reasons for non-participation included concerns about side effects, and the burden and cost of travel. Financial reimbursement for expenses was reported to be likely to increase trial engagement, Phase two trials were the most popular to participate in, and the use of a placebo arm was seen as a disincentive. Across all disease subgroups, drug repurposing trials proved popular and just under 70% of participants would be prepared to undergo intrathecal drug administration. Conclusions Knowledge of motivations for and barriers to trial participation as well as the acceptability of investigations, time commitments and routes of drug administration should inform better, more patient focused trial design. This in turn may improve recruitment and retention of participants to future trials

CERTAIN ENVIRONMENT, ECONOMIC AND SOCIAL ASPECTS OF THE HIGH NATURAL VALUE (HNV) FARMING: ROMANIAN’S STATE OF THE ART

HNV farming is a new concept that describes those farming systems in Europe that have the widest biodiversity. It brings an alternative and complementary approach to the typology that has become conventional by nature conservation. The paper presents the role of the HNV farming system for the conservation of rare and threatened species and habitats in protected areas as well as preservation of biodiversity inEurope, which largely depends on the continuation of traditional agricultural practices in much wider areas of European rural space. Thus, one of the major problems in the implementation of agricultural policies in many European countries has been made aware: support for ,,nature” focuses on ,,designated areas” while support for ,,agriculture” flows abundantly towards large, intensive producers. This situation needs to be reconsidered because in the distribution of European funds there has been a recommendation on the major change towards environmentally beneficial land use

The global prevalence of IBS in adults remains elusive due to the heterogeneity of studies: a Rome Foundation working team literature review

Objectives The global prevalence of IBS is difficult to ascertain, particularly in light of the heterogeneity of published epidemiological studies. The aim was to conduct a literature review, by experts from around the world, of community-based studies on IBS prevalence. Design Searches were conducted using predetermined search terms and eligibility criteria, including papers in all languages. Pooled prevalence rates were calculated by combining separate population survey prevalence estimates to generate an overall combined meta-prevalence estimate. The heterogeneity of studies was assessed. Results 1451 papers were returned and 83, including 288 103 participants in 41 countries, met inclusion criteria. The mean prevalence among individual countries ranged from 1.1% in France and Iran to 35.5% in Mexico. There was significant variance in pooled regional prevalence rates ranging from 17.5% (95% CI 16.9% to 18.2%) in Latin America, 9.6% (9.5% to 9.8%) in Asia, 7.1% (8.0% to 8.3%) in North America/Europe/Australia/New Zealand, to 5.8% (5.6% to 6.0%) in the Middle East and Africa. There was a significant degree of heterogeneity with the percentage of residual variation due to heterogeneity at 99.9%. Conclusions The main finding is the extent of methodological variance in the studies reviewed and the degree of heterogeneity among them. Based on this, we concluded that publication of a single pooled global prevalence rate, which is easily calculated, would not be appropriate or contributory. Furthermore, we believe that future studies should focus on regional and cross-cultural differences that are more likely to shed light on pathophysiology

Esophageal resection- experience in center of general surgery and liver transplantation “Dan Setlacec” 2001-2011

Institultul Clinic Fundeni, Clinica Chirurgie Generală şi Transplant Hepatic “Dan Setlacec”, Al XI-lea Congres al Asociației Chirurgilor „Nicolae Anestiadi” din Republica Moldova și cea de-a XXXIII-a Reuniune a Chirurgilor din Moldova „Iacomi-Răzeșu” 27-30 septembrie 2011Chirurgia rezecţională esofagiană este complexă prin varietatea substratului lezional, multitudinea căilor de abord precum şi terenul biologic frecvent alterat al pacienţilor. Scopul acestei lucrări este analiza rezultatelor postoperatorii imediate şi tardive în rândul pacienţilor supuşi rezecţiei esofagiene în Centrul de Chirurgie Generală şi Transplant Hepatic ”Dan Setlacec” în perioada 2001-2011.Esophageal resection is a demanding surgical task due to various lesional substrat and type of surgical approach and, often, .alterated biological field of patients.The aim of this study is to analyze immediate and late outcome of patients reffered for esophageal resection in Center for General Surgery and Liver Transplantation “Dan Setlacec” from Fundeni Clinical Institute along a decade (2001-2011)