Reviewerly/exHarmony huggingface dataset:
Self-supervised learning-based cervical cytology for the triage of HPV-positive women in resource-limited settings and low-data regime

Screening Papanicolaou test samples has proven to be highly effective in reducing cervical cancer-related mortality. However, the lack of trained cytopathologists hinders its widespread implementation in low-resource settings. Deep learning-assisted telecytology diagnosis emerges as an appealing alternative, but it requires the collection of large annotated training datasets, which is costly and time-consuming. In this paper, we demonstrate that the abundance of unlabeled images that can be extracted from Pap smear test whole slide images presents a fertile ground for self-supervised learning methods, yielding performance improvements compared to off-the-shelf pre-trained models for various downstream tasks. In particular, we propose Cervical Cell Copy-Pasting (C3P) as an effective augmentation method, which enables knowledge transfer from public and labeled single-cell datasets to unlabeled tiles. Not only does C3P outperforms naive transfer from single-cell images, but we also demonstrate its advantageous integration into multiple instance learning methods. Importantly, all our experiments are conducted on our introduced in-house dataset comprising liquid-based cytology Pap smear images obtained using low-cost technologies. This aligns with our long-term objective of deep learning-assisted telecytology for diagnosis in low-resource settings.
Exploring the Learning Difficulty of Data: Theory and Measure

‘‘Easy/hard sample” is a popular parlance in machine learning. Learning difficulty of samples refers to how easy/hard a sample is during a learning procedure. An increasing need of measuring learning difficulty demonstrates its importance in machine learning (e.g., difficulty-based weighting learning strategies). Previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, influential factors for learning difficulty are summarized. Under various situations conducted by summarized influential factors, correlations between learning difficulty and two vital criteria of machine learning, namely, generalization error and model complexity, are revealed. Second, a theoretical definition of learning difficulty is proposed on the basis of these two criteria. A practical measure of learning difficulty is proposed under the direction of the theoretical definition by importing the bias-variance trade-off theory. Subsequently, the rationality of theoretical definition and the practical measure are demonstrated, respectively, by analysis of several classical weighting methods and abundant experiments realized under all situations conducted by summarized influential factors. The mentioned weighting methods can be reasonably explained under the proposed theoretical definition and concerned propositions. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.
An Adaptive Variational Outlier-Robust Filter for Multisensor Distributed Fusion

The fusion accuracy of distributed fusion algorithms may degrade in multi-sensor measurements with unknown noise and outliers. To tackle the problem, an improved adaptive feedback covariance intersection fusion algorithm based on the modified slide window variational outlier-robust adaptive Kalman filter is proposed. To estimate unknown noise covariance and eliminate outliers, a modified slide window variational outlier-robust adaptive Kalman filter is proposed. The algorithm devises a simplified slide-window variational adaptive filter according to the Student’s t distribution, which treats the Student’s t distribution as the approximation of the posterior distribution to eliminate the effect of outliers. The adaptive factor is introduced into this algorithm to realize the tradeoff between measurement and prediction. Moreover, an improved adaptive feedback covariance intersection fusion algorithm is developed with respect to multisensor information fusion with uncertain noise. The simulations verify that the improved fusion algorithm outperforms other existing filtering and fusion algorithms.
A study on a recommendation algorithm based on spectral clustering and GRU

With the development of e-commerce, the importance of recommendation algorithms has significantly increased. However, traditional recommendation systems struggle to address issues such as data sparsity and cold start. This article proposes an optimization method for a recommendation system based on spectral clustering (SC) and gated recurrent unit (GRU), named the GRU-KSC algorithm. Firstly, this paper improves the original spectral clustering algorithm by introducing Kmc2, proposing a novel spectral clustering recommendation algorithm (K-means++ SC, KSC) based on the existing SC algorithm. Secondly, building upon the original GRU model, the paper presents a hybrid recommendation algorithm (Hybrid GRU, HGRU) capable of capturing long-term user interests for a more personalized recommendation. Experiments conducted on real datasets demonstrate that our method outperforms existing benchmark methods in terms of accuracy and robustness.
Contrastive image adaptation for acquisition shift reduction in medical imaging

The domain shift, or acquisition shift in medical imaging, is responsible for potentially harmful differences between development and deployment conditions of medical image analysis techniques. There is a growing need in the community for advanced methods that could mitigate this issue better than conventional approaches. In this paper, we consider configurations in which we can expose a learning-based pixel level adaptor to a large variability of unlabeled images during its training, i.e. sufficient to span the acquisition shift expected during the training or testing of a downstream task model. We leverage the ability of convolutional architectures to efficiently learn domain-agnostic features and train a many-to-one unsupervised mapping between a source collection of heterogeneous images from multiple unknown domains subjected to the acquisition shift and a homogeneous subset of this source set of lower cardinality, potentially constituted of a single image. To this end, we propose a new cycle-free image-to-image architecture based on a combination of three loss functions : a contrastive PatchNCE loss, an adversarial loss and an edge preserving loss allowing for rich domain adaptation to the target image even under strong domain imbalance and low data regimes. Experiments support the interest of the proposed contrastive image adaptation approach for the regularization of downstream deep supervised segmentation and cross-modality synthesis models.
An Assessment of Rules of Thumb for Software Phase Management, and the Relationship Between Phase Effort and Schedule Success

In the planning of a software development project, managers must estimate the amount of effort needed for distinct phases of activity. A number of rules of thumb exist in the literature to help the program manager in this task. However, very little work has been done to validate these rules of thumb. Applying least square models and Hotelling’s <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</i> <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> test, we evaluate these rules of thumb against a large database of Department of Defense projects. We determine that variability limits the simple application of any such rule. However, there are some worthy of closer attention, and we recommend adjustments for improved application. We also determine that projects which give extra attention to early phases experience less schedule growth. These findings were robust across developmental process type, military service, and project size.
Designing Electronic Data Capture Systems for Sustainability in Low-Resource Settings: Viewpoint With Lessons Learned From Ethiopia and Myanmar

Electronic data capture (EDC) is a crucial component in the design, evaluation, and sustainment of population health interventions. Low-resource settings, however, present unique challenges for developing a robust EDC system due to limited financial capital, differences in technological infrastructure, and insufficient involvement of those who understand the local context. Current literature focuses on the evaluation of health interventions using EDC but does not provide an in-depth description of the systems used or how they are developed. In this viewpoint, we present case descriptions from 2 low- and middle-income countries: Ethiopia and Myanmar. We address a gap in evidence by describing each EDC system in detail and discussing the pros and cons of different approaches. We then present common lessons learned from the 2 case descriptions as recommendations for considerations in developing and implementing EDC in low-resource settings, using a sociotechnical framework for studying health information technology in complex adaptive health care systems. Our recommendations highlight the importance of selecting hardware compatible with local infrastructure, using flexible software systems that facilitate communication across different languages and levels of literacy, and conducting iterative, participatory design with individuals with deep knowledge of local clinical and cultural norms.
Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging.However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data.In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis.To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm.To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans.For the real-world task, PPPML-HMI achieved ∼5% higher Dice score on average compared to conventional FL under the heterogeneous scenario.Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI.By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI.
Examining the role of self-reported somatosensory sensations in body (dis)ownership: A scoping review and empirical study of patients with a disturbed sense of limb ownership

Patients with a disturbed sense of limb ownership (DSO) offer a unique window of insight into the multisensory processes contributing to the sense of body ownership. A limited amount of past research has examined the role of sensory deficits in DSO, and even less is known regarding the role of patient self-reported somatosensory sensations in the pathogenesis of DSO. To address this lack of knowledge we first conducted a systematic scoping review following PRISMA-SR guidelines, examining current research into somatosensory deficits and patient self-reported somatosensory sensations in patients with DSO. Eighty studies, including 277 DSO patients, were identified. The assessment of sensory deficits was generally limited in scope and quality, and deficits in tactile sensitivity and proprioception were most frequently found. The reporting of somatosensory sensations was even less frequent, with instances of paraesthesia (pins-and-needles), stiffness/rigidity, numbness and warmth, coldness and heaviness amongst the deficits recorded. In a second part of the study, we sought to directly address the lack of evidence concerning the impact of patient self-reported somatosensory sensations in DSO by measuring DSO and self-reported somatosensory sensations in a large (n = 121) sample of right-hemisphere stroke patients including N = 65 with DSO and N = 56 hemiplegic controls. Results show that feelings of coldness and stiffness modulate DSO symptoms. Sense of heaviness and numbness are more frequent in patients with DSO but do not have a clear impact on disownership symptomology. Although preliminary, these results suggest a role of subjective sensations about the felt body in the sense of limb ownership.
Automatic data augmentation for medical image segmentation using Adaptive Sequence-length based Deep Reinforcement Learning

Although existing deep reinforcement learning-based approaches have achieved some success in image augmentation tasks, their effectiveness and adequacy for data augmentation in intelligent medical image analysis are still unsatisfactory. Therefore, we propose a novel Adaptive Sequence-length based Deep Reinforcement Learning (ASDRL) model for Automatic Data Augmentation (AutoAug) in intelligent medical image analysis. The improvements of ASDRL-AutoAug are two-fold: (i) To remedy the problem of some augmented images being invalid, we construct a more accurate reward function based on different variations of the augmentation trajectories. This reward function assesses the validity of each augmentation transformation more accurately by introducing different information about the validity of the augmented images. (ii) Then, to alleviate the problem of insufficient augmentation, we further propose a more intelligent automatic stopping mechanism (ASM). ASM feeds a stop signal to the agent automatically by judging the adequacy of image augmentation. This ensures that each transformation before stopping the augmentation can smoothly improve the model performance. Extensive experimental results on three medical image segmentation datasets show that (i) ASDRL-AutoAug greatly outperforms the state-of-the-art data augmentation methods in medical image segmentation tasks, (ii) the proposed improvements are both effective and essential for ASDRL-AutoAug to achieve superior performance, and the new reward evaluates the transformations more accurately than existing reward functions, and (iii) we also demonstrate that ASDRL-AutoAug is adaptive for different images in terms of sequence length, as well as generalizable across different segmentation models.
Enhancing patient education in cancer care: Intelligent cancer patient education model for effective communication

The incidence of cancer is on the rise annually, whereas there exists a significant deficit of healthcare personnel. Inadequate communication between healthcare providers and patients may result in adverse emotional outcomes for the latter and interfere with their treatment progress. A viable solution to alleviate patient distress involves utilizing text generation models as an efficacious tool for delivering patient education.In this study, we proposed an intelligent cancer patient education model (ICPEM) based on the pre-trained T5 model. Meanwhile, we presented a new method for optimizing the model's comprehension of the patient's intent through simulating the inquiries that the patient may ask. The datasets used include a doctor and patient dialogue dataset and a cancer patient education scenario dataset. After prompt-tuning, the model is capable of educating patients through four major aspects including medical examination, health care, radiotherapy, chemotherapy.We conducted a comprehensive evaluation of the model by employing both automated and manual metrics. Our findings indicate that the responses generated by the model effectively catered to the requirements of patient education. Furthermore, our visualization analysis demonstrated the model's proficiency in processing sentences that are prone to confusion while maintaining a robust comprehension of human intent. Finally, several shortcomings of the model are presented, such as the inadequate amount of knowledge and the limited range of responses.We proposed a method for training and deploying medical language models in a low-resource environment. The proposed model facilitated the comprehension of cancer patients' intentions, resulting in suitable responses, which promotes effective patient-provider communication.
Low-power laser images enhancement via deep feature recovery for HDR 3D measurement

Laser 3D measurement has gained widespread applications in industrial metrology . Still, it is usually limited by surfaces with high dynamic range (HDR) or the colorful surface texture of measured surfaces, such as metal and black industrial parts. Currently, conventional methods generally work with relatively strong-power laser intensities, which could potentially damage the sample or induce eye-safety concerns. For deep-learning-based methods, due to the different reflectivity of the measured surfaces, the HDR problem may require cumbersome adjustment of laser intensity in order to acquire enough training data. Even so, the problem of inaccurate ground truth may occur. To address these issues, this paper proposes the deep feature recovery (DFR) strategy to enhance low-light laser stripe images for achieving HDR 3D reconstruction with low cost, high robustness, and eye safety. To the best of our knowledge, this is the first attempt to tackle the challenge of high measurement costs associated with measuring HDR surfaces in laser 3D measurement. In learning the features of low-power laser images, the proposed strategy has a superior generalization ability and is insensitive to different low laser powers and variant surface reflectivity. To verify this point, we specially design the experiments by training the network merely using the diffusely reflective masks (DRM951) and testing the performance using diffusely reflective masks, metal surfaces, black industrial parts (contained in the constructed datasets DRO690, MO191, and BO107) and their hybrid scenes. Experimental results demonstrate that the proposed DFR strategy has good performances on robustness by testing different measurement scenes. For variously reflective surfaces, such as diffusely reflective surfaces, metal surfaces, and black parts surfaces, the reconstructed 3D shapes all have a similar quality to the reference method.
Weakly supervised image segmentation beyond tight bounding box annotations

Weakly supervised image segmentation approaches in the literature usually achieve high segmentation performance using tight bounding box supervision and decrease the performance greatly when supervised by loose bounding boxes. However, compared with loose bounding box, it is much more difficult to acquire tight bounding box due to its strict requirements on the precise locations of the four sides of the box. To resolve this issue, this study investigates whether it is possible to maintain good segmentation performance when loose bounding boxes are used as supervision. For this purpose, this work extends our previous parallel transformation based multiple instance learning (MIL) for tight bounding box supervision by integrating an MIL strategy based on polar transformation to assist image segmentation. The proposed polar transformation based MIL formulation works for both tight and loose bounding boxes, in which a positive bag is defined as pixels in a polar line of a bounding box with one endpoint located inside the object enclosed by the box and the other endpoint located at one of the four sides of the box. Moreover, a weighted smooth maximum approximation is introduced to incorporate the observation that pixels closer to the origin of the polar transformation are more likely to belong to the object in the box. The proposed approach was evaluated on two public datasets using dice coefficient when bounding boxes at different precision levels were considered in the experiments. The results demonstrate that the proposed approach achieves state-of-the-art performance for bounding boxes at all precision levels and is robust to mild and moderate errors in the loose bounding box annotations. The codes are available at https://github.com/wangjuan313/wsis-beyond-tightBB.
A bi-annotated Malay-English code-switching (Manglish) dataset of X posts for biological gender identification and authorship attribution

Low-resource languages, like Malay, face the threat of extinction when linguistic resources become scarce. This paper addresses the scarcity issue by contributing to the inventory of low-resource languages, specifically focusing on Malay-English, known as Manglish. Manglish speakers are primarily located in Malaysia, Indonesia, Brunei, and Singapore. As global adoption of second languages and social media usage increases, language code-switching, such as Spanglish and Chinglish, becomes more prevalent. In the case of Malay-English, this phenomenon is termed Manglish. To enhance the status of the Malay language and its transition out of the low-resource category, this unique text corpus, with binary annotations for biological gender and anonymized author identities is presented. This bi-annotated dataset offers valuable applications for various fields, including the investigation of cyberbullying, combating gender bias, and providing targeted recommendations for gender-specific products. This corpus can be used with either of the annotations or their composite. The dataset comprises of posts from 50 Malaysian public figures, equally split between biological males and females. The dataset contains a total of 709,012 raw X posts (formerly Twitter), with a relatively balanced distribution of 53.72% from biological female authors and 46.28% from biological male authors. Twitter API was used to scrape the posts. After pre-processing, the total posts reduced to 650,409 posts, widening the gap between the genders with the 56.88% for biological female and 43.12% for biological male. This dataset is a valuable resource for researchers in the field of Malay-English code-switching Natural Language Processing (NLP) and can be used to train or enhance existing and future Manglish language transformers.
Unsupervised topological analysis of polarized light microscopy: application to quantitative birefringence imaging

The determination of birefringence (magnitude and axis orientation) of optical materials is of significant interest in various fields. In the case of composite samples, this task becomes complicated and time-consuming; therefore, a partially automated procedure for reconstructing birefringence spatial distribution becomes valuable. Herein, we propose a procedure to reconstruct the spatial distributions of the retardance and optical axis orientation in a geological thin section from sparse quantitative birefringence measurements, using automatic boundary detection on cross-polarized light microscopy images. We examine two particular areas on the selected geological thin section: one that presents a uniaxial crystal with a circular cross-section of its refractive index ellipsoid and the other with grains of varying orientations. The measurement gives the orientation of the grain’s optical axis both in and out of the plane of the thin section, which explains the qualitative observations with the cross-polarized light microscope. Future work will connect the measured orientation of the rock thin section with its 3D geological orientation in the field.