In a novel demonstration, we combine these two new components and show logit mimicking exceeding feature imitation for the first time. The absence of localization distillation is a key explanation for the long-standing underperformance of logit mimicking. The thorough research underscores the remarkable potential of logit mimicking to alleviate localization uncertainty, learning robust feature representations, and making the initial training less burdensome. Furthermore, we establish a theoretical link between the suggested LD and the classification KD, demonstrating their shared optimizing effects. The simplicity and effectiveness of our distillation scheme make it readily adaptable to both dense horizontal object detectors and rotated object detectors. Experiments conducted on the MS COCO, PASCAL VOC, and DOTA datasets demonstrate that our approach yields significant improvements in average precision without negatively affecting inference speed. https://github.com/HikariTJU/LD contains our publicly shared source code and pre-trained models.
As techniques for automated design and optimization, network pruning and neural architecture search (NAS) are applicable to artificial neural networks. Instead of the traditional approach of training and then pruning, this paper advocates for a simultaneous search and training methodology to create a compact network directly from initial design. Utilizing pruning as a search technique, we present three novel insights for network engineering: 1) crafting adaptive search as a cold-start approach to uncover a reduced sub-network on a large scale; 2) autonomously determining the threshold for network pruning; 3) enabling the flexibility to prioritize either efficiency or robustness. From a more specific standpoint, we propose an adaptive search algorithm, applied to the cold start, that takes advantage of the inherent randomness and flexibility of filter pruning mechanisms. ThreshNet, a flexible coarse-to-fine pruning method drawing inspiration from reinforcement learning, will update the weights associated with the network filters. We further introduce a robust pruning strategy, utilizing knowledge distillation through the mechanism of a teacher-student network. In a series of tests encompassing ResNet and VGGNet models, our proposed method has been shown to achieve a superior trade-off between performance and resource utilization compared to current leading pruning techniques, resulting in marked improvements on benchmark datasets like CIFAR10, CIFAR100, and ImageNet.
Abstract data representations, increasingly prevalent in scientific pursuits, enable novel interpretive approaches and conceptual frameworks for understanding phenomena. Researchers gain fresh insights and targeted study directions by moving from unprocessed image pixels to segmented and reconstructed objects. Therefore, the pursuit of novel and enhanced segmentation methodologies continues as a vibrant area of research. Employing deep neural networks, like U-Net, scientists have been actively engaged in achieving pixel-level segmentations, a process facilitated by advancements in machine learning and neural networks. This involves linking pixels to their corresponding objects and subsequently collecting these objects. First establishing geometric priors, then applying machine learning for classification, represents an alternative method; topological analysis, notably the use of the Morse-Smale complex to encode areas of consistent gradient flow behavior, offers this alternative strategy. This empirically driven approach is justified by the common occurrence of phenomena of interest appearing as subsets of topological priors in diverse applications. Employing topological elements not only streamlines the learning process by decreasing the learning space, but also empowers the model with learnable geometries and connectivity, facilitating the classification of segmentation targets. This paper describes a method for building learnable topological elements, explores the usage of machine learning techniques for classification in numerous areas, and showcases this technique as a viable alternative to pixel-based classification with similar levels of accuracy, enhanced processing speed, and a reduced training dataset requirement.
A portable kinetic perimeter, automated and VR-headset based, is introduced as a novel and alternative method for evaluating clinical visual fields. To validate our solution's performance, we measured against a gold standard perimeter, employing healthy subjects in the test.
The system utilizes an Oculus Quest 2 VR headset, with a clicker mechanism for real-time participant response feedback. To follow the Goldmann kinetic perimetry standard, a Unity app for Android was created to generate stimuli moving along defined vectors. Sensitivity thresholds are determined by the centripetal movement of three distinct targets (V/4e, IV/1e, III/1e) along 12 or 24 vectors, progressing from an area of no sight to an area of sight, and subsequently wirelessly sent to a personal computer. The hill of vision, as depicted in a two-dimensional isopter map, is dynamically generated by a Python real-time algorithm processing the incoming kinetic results. For our proposed solution, 21 participants (5 males, 16 females, aged 22-73) were assessed, resulting in 42 eyes examined. Reproducibility and effectiveness were evaluated by comparing the results with a Humphrey visual field analyzer.
A strong agreement existed between isopters generated by the Oculus headset and those captured by a commercial device, as indicated by Pearson's correlation values exceeding 0.83 for each target.
Our VR kinetic perimetry system's performance is examined and contrasted with a widely used clinical perimeter in a study involving healthy participants.
The proposed device paves the way for a more accessible and portable visual field test, overcoming the limitations of current kinetic perimetry.
The proposed device paves the way for a more accessible and portable visual field test, transcending the limitations of existing kinetic perimetry methods.
Explaining the causal basis of predictions is vital for transforming the success of deep learning-based computer-assisted classification into a clinically applicable tool. medicine bottles The technical and psychological efficacy of post-hoc interpretability approaches, especially when employing counterfactual methods, is notable. However, current dominant approaches implement heuristic, unconfirmed methodologies. Therefore, their operation of the underlying networks, exceeding their approved parameters, raises questions about the predictor's reliability rather than fostering knowledge and trust. Utilizing marginalization strategies and evaluation procedures, this research investigates the out-of-distribution predicament encountered by medical image pathology classifiers. DNA inhibitor Further to this, we detail a complete and domain-sensitive pipeline for radiology imaging procedures. The proposed method's validity is substantiated by testing on a synthetic dataset and two publicly accessible image data sets. Evaluation was conducted using the CBIS-DDSM/DDSM mammography collection and the Chest X-ray14 radiographs. Our solution demonstrates a substantial decrease in localization ambiguity, both quantitatively and qualitatively, yielding clearer results.
A critical aspect of leukemia classification is the detailed cytomorphological examination of a Bone Marrow (BM) smear sample. In spite of this, the implementation of established deep learning methods suffers from two major obstacles. To achieve meaningful results, these methodologies rely on comprehensive datasets with expert-level annotations at the cell level, but usually exhibit poor performance when applied more broadly. Furthermore, BM cytomorphological examination is treated as a multi-class cell classification problem, neglecting the interconnectedness of leukemia subtypes across various hierarchical levels. Hence, the manual evaluation of BM cytomorphology, a laborious and repetitive task, is still undertaken by expert cytologists. Recent progress in Multi-Instance Learning (MIL) has facilitated data-efficient medical image processing, drawing on patient-level labels discernible within clinical reports. We introduce a hierarchical framework for Multi-Instance Learning (MIL), incorporating Information Bottleneck (IB) mechanisms, to address the limitations previously stated. In order to process the patient-level label, our hierarchical MIL framework employs attention-based learning to identify cells possessing high diagnostic value for leukemia classification across different hierarchies. Following the guidance of the information bottleneck principle, we propose a hierarchical IB method that refines and restricts representations across distinct hierarchical levels, thereby yielding higher accuracy and broader generalization. We leverage our framework on a comprehensive dataset of childhood acute leukemia cases, detailed with bone marrow smear images and clinical histories, to highlight its ability to detect diagnostic cells autonomously, without resorting to cell-level annotations, thereby exceeding alternative comparative methods. Additionally, the evaluation performed on a different test set confirms the wide applicability of our framework.
Wheezes, characteristic adventitious respiratory sounds, are commonly observed in patients with respiratory conditions. Understanding the presence and temporal location of wheezes is clinically important, providing insight into the degree of bronchial obstruction. Conventional auscultation is a standard technique for evaluating wheezes, but remote monitoring is rapidly becoming essential during this time. mixture toxicology Automatic respiratory sound analysis is a prerequisite for the successful performance of remote auscultation. A wheezing segmentation approach is put forth in this study. Our method starts by using empirical mode decomposition to break down a given audio excerpt into intrinsic mode frequencies. Applying harmonic-percussive source separation to the resulting audio streams yields harmonic-enhanced spectrograms, which are subsequently processed to produce harmonic masks. Following that, a progression of rules, built upon empirical data, is used to locate probable wheezing events.