Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, ...guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data.
Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS.
The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.
Recently, self-supervised pretraining of transformers has gained considerable attention in analyzing electronic medical records. However, systematic evaluation of different pretraining tasks in ...radiology applications using both images and radiology reports is still lacking. We propose PreRadE, a simple proof of concept framework that enables novel evaluation of pretraining tasks in a controlled environment. We investigated three most-commonly used pretraining tasks (MLM—Masked Language Modelling, MFR—Masked Feature Regression, and ITM—Image to Text Matching) and their combinations against downstream radiology classification on MIMIC-CXR, a medical chest X-ray imaging and radiology text report dataset. Our experiments in the multimodal setting show that (1) pretraining with MLM yields the greatest benefit to classification performance, largely due to the task-relevant information learned from the radiology reports. (2) Pretraining with only a single task can introduce variation in classification performance across different fine-tuning episodes, suggesting that composite task objectives incorporating both image and text modalities are better suited to generating reliably performant models.
In the last decade, there has been huge advancement in biomechatronic systems by the integration of pattern recognition and regression algorithms. In many myoelectric control studies, high accuracy ...in estimating a subject’s wrist movement was reported by measuring electromyography (EMG) signal from subjects’ forearms. However, many algorithms suffer from limited robustness against undesired disturbance in the real-world environment. In particular, arm position change is an inevitable disturbance that results in severe degradation of performance. In this study, the weighted recursive Gaussian process (WRGP) is proposed to overcome this effect. In the algorithm, the noise variance is weighted by covariate shift adaptation, which is able to handle the uncertainties. WRGP is compared with the commonly used linear regression (LR) and multilayer perceptron (MLP). LR, MLP, and WRGP are trained with the EMG dataset acquired at an arm position and tested with the different EMG dataset acquired at another arm position. Also, WRGP with uniform weights and WRGP with the weights estimated from the covariate shift adaptation are compared. The results show that WRGP with uniform weights is more robust than LR and MLP regardless of the arm position (0.16 and 0.16 higher average
R
2
index, respectively). The performance of WRGP with the weights estimated from the covariate shift adaptation is significantly higher (
p
<
0.05
) than the performance of LR and MLP (0.30 and 0.33 higher average
R
2
index, respectively).
Distal radius (wrist) fractures are the second most common fracture admitted to hospital. The anatomical pattern of these types of injuries is diverse, with variation in clinical management, ...guidelines for management remain inconclusive, and the uptake of findings from clinical trials into routine practice limited. Robust predictive modelling, which considers both the characteristics of the fracture and patient, provides the best opportunity to reduce variation in care and improve patient outcomes. This type of data is housed in unstructured data sources with no particular format or schema. The "Predicting fracture outcomes from clinical Registry data using Artificial Intelligence (AI) Supplemented models for Evidence-informed treatment (PRAISE)" study aims to use AI methods on unstructured data to describe the fracture characteristics and test if using this information improves identification of key fracture characteristics and prediction of patient-reported outcome measures and clinical outcomes following wrist fractures compared to prediction models based on standard registry data. Adult (16+ years) patients presenting to the emergency department, treated in a short stay unit, or admitted to hospital for >24h for management of a wrist fracture in four Victorian hospitals will be included in this study. The study will use routine registry data from the Victorian Orthopaedic Trauma Outcomes Registry (VOTOR), and electronic medical record (EMR) information (e.g. X-rays, surgical reports, radiology reports, images). A multimodal deep learning fracture reasoning system (DLFRS) will be developed that reasons on EMR information. Machine learning prediction models will test the performance with/without output from the DLFRS. The PRAISE study will establish the use of AI techniques to provide enhanced information about fracture characteristics in people with wrist fractures. Prediction models using AI derived characteristics are expected to provide better prediction of clinical and patient-reported outcomes following distal radius fracture.
Contrastive learning has gained widespread adoption for retrieval tasks due to its minimal requirement for manual annotations. However, popular contrastive frameworks typically learn from binary ...relevance, making them ineffective at incorporating direct fine-grained rankings. In this paper, we curate a large-scale dataset featuring detailed relevance scores for each query-document pair to facilitate future research and evaluation. Subsequently, we propose Generalized Contrastive Learning for Multi-Modal Retrieval and Ranking (GCL), which is designed to learn from fine-grained rankings beyond binary relevance scores. Our results show that GCL achieves a 94.5% increase in NDCG@10 for in-domain and 26.3 to 48.8% increases for cold-start evaluations, all relative to the CLIP baseline and involving ground truth rankings.
Uncertainty estimation is an important research area to make deep neural networks (DNNs) more trustworthy. While extensive research on uncertainty estimation has been conducted with unimodal data, ...uncertainty estimation for multimodal data remains a challenge. Neural processes (NPs) have been demonstrated to be an effective uncertainty estimation method for unimodal data by providing the reliability of Gaussian processes with efficient and powerful DNNs. While NPs hold significant potential for multimodal uncertainty estimation, the adaptation of NPs for multimodal data has not been carefully studied. To bridge this gap, we propose Multimodal Neural Processes (MNPs) by generalising NPs for multimodal uncertainty estimation. Based on the framework of NPs, MNPs consist of several novel and principled mechanisms tailored to the characteristics of multimodal data. In extensive empirical evaluation, our method achieves state-of-the-art multimodal uncertainty estimation performance, showing its appealing robustness against noisy samples and reliability in out-of-distribution detection with faster computation time compared to the current state-of-the-art multimodal uncertainty estimation method.
Resolving conflicts is essential to make the decisions of multi-view classification more reliable. Much research has been conducted on learning consistent informative representations among different ...views, assuming that all views are identically important and strictly aligned. However, real-world multi-view data may not always conform to these assumptions, as some views may express distinct information. To address this issue, we develop a computational trust-based discounting method to enhance the existing trustworthy framework in scenarios where conflicts between different views may arise. Its belief fusion process considers the trustworthiness of predictions made by individual views via an instance-wise probability-sensitive trust discounting mechanism. We evaluate our method on six real-world datasets, using Top-1 Accuracy, AUC-ROC for Uncertainty-Aware Prediction, Fleiss' Kappa, and a new metric called Multi-View Agreement with Ground Truth that takes into consideration the ground truth labels. The experimental results show that computational trust can effectively resolve conflicts, paving the way for more reliable multi-view classification models in real-world applications.
Prompt learning has shown to be an efficient and effective fine-tuning method
for vision-language models like CLIP. While numerous studies have focused on
the generalisation of these models in ...few-shot classification, their capability
in near out-of-distribution (OOD) detection has been overlooked. A few recent
works have highlighted the promising performance of prompt learning in far OOD
detection. However, the more challenging task of few-shot near OOD detection
has not yet been addressed. In this study, we investigate the near OOD
detection capabilities of prompt learning models and observe that commonly used
OOD scores have limited performance in near OOD detection. To enhance the
performance, we propose a fast and simple post-hoc method that complements
existing logit-based scores, improving near OOD detection AUROC by up to 11.67%
with minimal computational cost. Our method can be easily applied to any prompt
learning model without change in architecture or re-training the models.
Comprehensive empirical evaluations across 13 datasets and 8 models demonstrate
the effectiveness and adaptability of our method.
Generally, Least Squares (LS) Method treats only random errors of observation vector in adjustment function models. However, both observation vector and elements of coefficient matrix of adjustment ...function model contain random errors. Therefore, there is no guarantee that the result of adjustment by LS method is the global optimal solution. Total Least Square (TLS) method is a primary estimation method that treats random errors of observation vector and coefficient matrix in adjustment functional models. Since TLS method take into account both random errors of observation vector and coefficient matrix based on errors-in-variables model, it is possible to improve the accuracy compared with the result of LS method. So TLS method has been applied to different fields of science and technology including signal and image processing, computer vision,communication engineering and geodesy. However, weighted total least square (WTLS) method has been not applied in geodetic network adjustment problem compared with other fields widely. So the purpose of this paper is to summarize the algorithm of WTLS briefly and to propose an application method in adjustment of triangulation network. Key problem in application of WTLS to adjustment of geodetic network is to determine the weight matrix (or cofactor matrix) for elements of coefficient matrix in adjustment function model. In this paper proposed a method to determine cofactor matrix for errors of coefficient matrix in triangulation network, and verifies the effectiveness of suggested method through example applied to triangulation network.
The objective of this study was to evaluate the effect of roscovitine pretreatment on the number of matured oocytes per ovary available for somatic cell nuclear transfer (SCNT) and their ...developmental competence. Irrespective of reproduction status (prepuberty/puberty), the average number of small follicles per ovary (19.3/17.2) was higher than that of medium follicles (1.5/2.7). In the small follicle group, the in vitro maturation rate of COCs pretreated with 50 μM roscovitine (56.1%) was significantly (P < 0.05) higher than that of the control, 25 or 75 μM treatment (15.5%, 23.7% and 35.2%, respectively), while, in the medium follicle group, there was no significant difference between the control, 25, 50 and 75 μM treatment (76.4%, 78.3%, 80.9% and 60.6%, respectively). As a result, a total number of matured oocytes per ovary was greater for 50 μM treatment (11.8) than for the control, 25 or 75 μM treatment (4.4, 5.0 and 6.3, respectively). In the small follicle group, COCs pretreated with 50 μM roscovitine showed dramatically increased blastocyst rate (16.0%) compared to the control (2.9%) (P < 0.05), whereas, in the medium follicle group, there was no significant difference between groups independent of roscovitine treatment (20.8 vs 23.0%). The cloning efficiency in the roscovitine-treated group was not significantly different from that in the control (2.6 vs 1.4%). In conclusion, the present study indicates that roscovitine treatment increased the number of matured oocytes per ovary available for SCNT and did not have any adverse effect on cloning efficiency in pigs.
•Roscovitine pretreatment increased the availability of oocytes for SCNT.•In vitro developmental competence of small follicle-derived oocytes was improved.•Normal cloned piglets were generated from roscovitine-pretreated oocytes.
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