Visual Boundary-Guided Pseudo-Labeling for Weakly Supervised 3D Point Cloud Segmentation in Indoor Environments.

Accurate segmentation of 3D point clouds in indoor scenes remains a challenging task, often hindered by the labor-intensive nature of data annotation. While weakly supervised learning approaches have shown promise in leveraging partial annotations, they frequently struggle with imbalanced performance between foreground and background elements due to the complex structures and proximity of objects in indoor environments. To address this issue, we propose a novel foreground-aware label enhancement method utilizing visual boundary priors.

Strategies to Improve Clinical Outcomes and Patient Experience Undergoing Transurethral Resection of Bladder Tumor.

Purpose Of Review: To describe patient experiences of transurethral resection of bladder tumor (TURBT) and review recent advances in enhancing clinical outcomes.

Recent Findings: High rates of recurrence and progression of non-muscle invasive bladder tumors expose patients to multiple TURBT procedures throughout their disease process. Understanding the impact of TURBT on quality of life and patient experiences is crucial for shared decision-making, thus enhanced recovery protocol trials are being explored to improve patient outcomes.

Evolution of wheat blast resistance gene Rmg8 accompanied by differentiation of variants recognizing the powdery mildew fungus.

Wheat blast, a devastating disease having spread recently from South America to Asia and Africa, is caused by Pyricularia oryzae (synonym of Magnaporthe oryzae) pathotype Triticum, which first emerged in Brazil in 1985. Rmg8 and Rmg7, genes for resistance to wheat blast found in common wheat and tetraploid wheat, respectively, recognize the same avirulence gene, AVR-Rmg8. Here we show that an ancestral resistance gene, which had obtained an ability to recognize AVR-Rmg8 before the differentiation of Triticum and Aegilops, has expanded its target pathogens.

Advancing Real-World Image Dehazing: Perspective, Modules, and Training.

Restoring high-quality images from degraded hazy observations is a fundamental and essential task in the field of computer vision. While deep models have achieved significant success with synthetic data, their effectiveness in real-world scenarios remains uncertain. To improve adaptability in real-world environments, we construct an entirely new computational framework by making efforts from three key aspects: imaging perspective, structural modules, and training strategies.

Predicting prostate cancer grade reclassification on active surveillance using a deep learning-based grading algorithm.

Deep learning (DL)-based algorithms to determine prostate cancer (PCa) Grade Group (GG) on biopsy slides have not been validated by comparison to clinical outcomes. We used a DL-based algorithm, AIRAProstate, to regrade initial prostate biopsies in 2 independent PCa active surveillance (AS) cohorts. In a cohort initially diagnosed with GG1 PCa using only systematic biopsies (n = 138), upgrading of the initial biopsy to ≥GG2 by AIRAProstate was associated with rapid or extreme grade reclassification on AS (odds ratio = 3.

Highly Efficient and Unsupervised Framework for Moving Object Detection in Satellite Videos.

Moving object detection in satellite videos (SVMOD) is a challenging task due to the extremely dim and small target characteristics. Current learning-based methods extract spatio-temporal information from multi-frame dense representation with labor-intensive manual labels to tackle SVMOD, which needs high annotation costs and contains tremendous computational redundancy due to the severe imbalance between foreground and background regions. In this paper, we propose a highly efficient unsupervised framework for SVMOD.

Enhancing Information Maximization With Distance-Aware Contrastive Learning for Source-Free Cross-Domain Few-Shot Learning.

Existing Cross-Domain Few-Shot Learning (CDFSL) methods require access to source domain data to train a model in the pre-training phase. However, due to increasing concerns about data privacy and the desire to reduce data transmission and training costs, it is necessary to develop a CDFSL solution without accessing source data. For this reason, this paper explores a Source-Free CDFSL (SF-CDFSL) problem, in which CDFSL is addressed through the use of existing pretrained models instead of training a model with source data, avoiding accessing source data.

Boosting Convolutional Neural Networks With Middle Spectrum Grouped Convolution.

This article proposes a novel module called middle spectrum grouped convolution (MSGC) for efficient deep convolutional neural networks (DCNNs) with the mechanism of grouped convolution. It explores the broad "middle spectrum" area between channel pruning and conventional grouped convolution. Compared with channel pruning, MSGC can retain most of the information from the input feature maps due to the group mechanism; compared with grouped convolution, MSGC benefits from the learnability, the core of channel pruning, for constructing its group topology, leading to better channel division.

Radiomics Signature Based on Support Vector Machines for the Prediction of Pathological Complete Response to Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer.

The objective of this study was to evaluate the discriminative capabilities of radiomics signatures derived from three distinct machine learning algorithms and to identify a robust radiomics signature capable of predicting pathological complete response (pCR) after neoadjuvant chemoradiotherapy in patients diagnosed with locally advanced rectal cancer (LARC). In a retrospective study, 211 LARC patients were consecutively enrolled and divided into a training cohort ( = 148) and a validation cohort ( = 63). From pretreatment contrast-enhanced planning CT images, a total of 851 radiomics features were extracted.

Lightweight Pixel Difference Networks for Efficient Visual Representation Learning.

Recently, there have been tremendous efforts in developing lightweight Deep Neural Networks (DNNs) with satisfactory accuracy, which can enable the ubiquitous deployment of DNNs in edge devices. The core challenge of developing compact and efficient DNNs lies in how to balance the competing goals of achieving high accuracy and high efficiency. In this paper we propose two novel types of convolutions, dubbed Pixel Difference Convolution (PDC) and Binary PDC (Bi-PDC) which enjoy the following benefits: capturing higher-order local differential information, computationally efficient, and able to be integrated with existing DNNs.

Dual-Function Smart Windows Using Polymer Stabilized Cholesteric Liquid Crystal Driven with Interdigitated Electrodes.

In this study, we present an electrically switchable window that can dynamically transmit both visible light and infrared (IR) light. The window is based on polymer stabilized cholesteric liquid crystals (PSCLCs), which are placed between a top plate electrode substrate and a bottom interdigitated electrode substrate. By applying a vertical alternating current electric field between the top and bottom substrates, the transmittance of the entire visible light can be adjusted.

Environmental impact of single-use and reusable flexible cystoscopes.

Objectives: To compare the carbon footprint and environmental impact of single-use and reusable flexible cystoscopes.

Materials And Methods: We analysed the expected clinical lifecycle of single-use (Ambu aScope™ 4 Cysto) and reusable (Olympus CYF-V2) flexible cystoscopes, from manufacture to disposal. Performance data on cumulative procedures between repairs and before decommissioning were derived from a high-volume multispecialty practice.

RobustFusion: Robust Volumetric Performance Reconstruction Under Human-Object Interactions From Monocular RGBD Stream.

High-quality 4D reconstruction of human performance with complex interactions to various objects is essential in real-world scenarios, which enables numerous immersive VR/AR applications. However, recent advances still fail to provide reliable performance reconstruction, suffering from challenging interaction patterns and severe occlusions, especially for the monocular setting. To fill this gap, in this paper, we propose RobustFusion, a robust volumetric performance reconstruction system for human-object interaction scenarios using only a single RGBD sensor, which combines various data-driven visual and interaction cues to handle the complex interaction patterns and severe occlusions.

Active Surveillance versus Immediate Intervention for Small Renal Masses: A Cost-Effectiveness and Clinical Decision Analysis.

Purpose: Active surveillance (AS) with the possibility of delayed intervention (DI) is emerging as a safe alternative to immediate intervention for many patients with small renal masses (SRMs). However, limited comparative data exist to inform the most appropriate management strategy for SRMs.

Materials And Methods: Decision analytic Markov modeling was performed to estimate the health outcomes and costs of 4 management strategies for 65-year-old patients with an incidental SRM: AS (with possible DI), immediate partial nephrectomy, radical nephrectomy, and thermal ablation.

Patient and in-hospital predictors of post-discharge opioid utilization: Individualizing prescribing after radical prostatectomy based on the ORIOLES initiative.

Objective: Judicious opioid stewardship would match each patient's prescription to their true medical necessity. However, most prescribing paradigms apply preset quantities and clinical judgment without objective data to predict individual use. We evaluated individual patient and in-hospital parameters as predictors of post-discharge opioid utilization after radical prostatectomy (RP) to provide evidence-based guidance for individualized prescribing.

Binary Neural Network for Automated Visual Surface Defect Detection.

As is well-known, defects precisely affect the lives and functions of the machines in which they occur, and even cause potentially catastrophic casualties. Therefore, quality assessment before mounting is an indispensable requirement for factories. Apart from the recognition accuracy, current networks suffer from excessive computing complexity, making it of great difficulty to deploy in the manufacturing process.

Self-reported quality of life as a predictor of mortality in renal cell carcinoma.

Background: This study evaluated the utility of self-reported quality of life (QOL) metrics in predicting mortality among all-comers with renal cell carcinoma (RCC) and externally tested the findings in a registry of patients with small renal masses.

Methods: The Surveillance, Epidemiology, and End Results-Medicare Health Outcomes Survey (SEER-MHOS) captured QOL metrics composed of mental component summary (MCS) and physical component summary (PCS) scores. Regression models assessed associations of MCS and PCS with all-cause, RCC-specific, and non-RCC-specific mortality.

Using Competing Risk of Mortality to Inform the Transition from Prostate Cancer Active Surveillance to Watchful Waiting.

Background: For men on active surveillance (AS) for prostate cancer (PCa), disease progression and age-related changes in health may influence decisions about pursuing curative treatment.

Objective: To evaluate the predicted PCa and non-PCa mortality at the time of reclassification among men on AS, to identify clinical criteria for considering a transition from AS to watchful waiting (WW).

Design, Setting, And Participants: Patients enrolled in a large AS program who experienced biopsy grade reclassification (Gleason grade increase) were retrospectively examined.

A micro-costing analysis of outpatient flexible cystoscopy: implications for adoption of single-use flexible cystoscopes.

Purpose: To evaluate the total cost of outpatient flexible cystoscopy associated with reusable device purchase, maintenance, and reprocessing, and to assess potential cost benefits of single-use flexible cystoscopes.

Methods: Cost data regarding the purchasing, maintaining, and reprocessing of reusable flexible cystoscopes were collected using a micro-costing approach at a high-volume outpatient urology clinic. We estimated the costs to facilities with a range of annual procedure volumes (1000-3000) performed with a fleet of cystoscopes ranging from 10 to 25.

Assessing quality of care in the diagnosis and treatment of early-stage testicular cancer: A critical review and summary.

Objective: To review the current literature on quality of care in the diagnosis and management of early-stage testicular cancer.

Methods: PubMed, Embase, and the Cochrane Central Register of Controlled Trials were searched for studies on quality of care in testicular cancer diagnosis and management from January 1980 to August 2018. Major overlapping themes related to quality of care in the diagnosis and management of TGCT were identified and evidence related to these themes were abstracted.