Self-Supervised Learning for Spinal MRIs

A significant proportion of patients scanned in a clinical setting have follow-up scans. We show in this work that such longitudinal scans alone can be used as a form of 'free' self-supervision for training a deep network. We demonstrate this self-supervised learning for the case of T2-weighted sagittal lumbar Magnetic Resonance Images (MRIs). A Siamese convolutional neural network (CNN) is trained using two losses: (i) a contrastive loss on whether the scan is of the same person (i.e. longitudinal) or not, together with (ii) a classification loss on predicting the level of vertebral bodies. The performance of this pre-trained network is then assessed on a grading classification task. We experiment on a dataset of 1016 subjects, 423 possessing follow-up scans, with the end goal of learning the disc degeneration radiological gradings attached to the intervertebral discs. We show that the performance of the pre-trained CNN on the supervised classification task is (i) superior to that of a network trained from scratch; and (ii) requires far fewer annotated training samples to reach an equivalent performance to that of the network trained from scratch.Comment: 3rd Workshop on Deep Learning in Medical Image Analysi

You said that?

We present a method for generating a video of a talking face. The method takes as inputs: (i) still images of the target face, and (ii) an audio speech segment; and outputs a video of the target face lip synched with the audio. The method runs in real time and is applicable to faces and audio not seen at training time. To achieve this we propose an encoder-decoder CNN model that uses a joint embedding of the face and audio to generate synthesised talking face video frames. The model is trained on tens of hours of unlabelled videos. We also show results of re-dubbing videos using speech from a different person.Comment: https://youtu.be/LeufDSb15Kc British Machine Vision Conference (BMVC), 201

Vision-Language Modelling For Radiological Imaging and Reports In The Low Data Regime

This paper explores training medical vision-language models (VLMs) -- where the visual and language inputs are embedded into a common space -- with a particular focus on scenarios where training data is limited, as is often the case in clinical datasets. We explore several candidate methods to improve low-data performance, including: (i) adapting generic pre-trained models to novel image and text domains (i.e. medical imaging and reports) via unimodal self-supervision; (ii) using local (e.g. GLoRIA) & global (e.g. InfoNCE) contrastive loss functions as well as a combination of the two; (iii) extra supervision during VLM training, via: (a) image- and text-only self-supervision, and (b) creating additional positive image-text pairs for training through augmentation and nearest-neighbour search. Using text-to-image retrieval as a benchmark, we evaluate the performance of these methods with variable sized training datasets of paired chest X-rays and radiological reports. Combined, they significantly improve retrieval compared to fine-tuning CLIP, roughly equivalent to training with the data. A similar pattern is found in the downstream task classification of CXR-related conditions with our method outperforming CLIP and also BioVIL, a strong CXR VLM benchmark, in the zero-shot and linear probing settings. We conclude with a set of recommendations for researchers aiming to train vision-language models on other medical imaging modalities when training data is scarce. To facilitate further research, we will make our code and models publicly available.Comment: Accepted to MIDL 202

An analysis of the legislative framework for mobile digital signature in Malaysia / Erickka Farrise Amir ... [et al.]

This research provides an analysis on the Legislative Framework for mobile digital signature in Malaysia. This analysis focuses on the issue whether the legislative framework for digital signature is adequate in governing mobile digital signature in Malaysia. This research is significant in view of the fact that electronic commerce transactions are on the verge of advancements in Malaysia nowadays. The law referred for this study are namely, the Digital Signature Act 1997, the Digital Signature Regulation 1998 and the Electronic Commerce Act 2006. Essentially, in comparative analysis, the laws in other countries are also referred to such as the Korean Digital Signature Act 2001 and Taiwan Electronic Signature Act. A series of literature are reviewed in this research as we analysed the comparisons between the Malaysian laws and the laws in other jurisdictions. As this topic of research is rather complex as it relates to the understanding of mobile digital signature technology, the comparative analysis would be a great help. This research proposes that the current legislative framework is inadequate in governing the issue of mobile digital signature in Malaysia. There are several recommendations provided by this research ranging from the amendment to the Digital Signature Act 1997, the improvement on the procedural aspect of the law, the enhancement on the role of judiciary and legal practitioners to the suggestion of alternative dispute resolutions. It is hoped that the findings of this research would provide a better understanding on the laws governing mobile digital signature. It is also our hope that this research would assist the policy makers in a review of the current legislative framework for mobile digital signature in Malaysia

Lubang Indah: The pothole installation art

The effects of the epidemic of COVID-19 can be seen in the natural deterioration of the roads. Self-isolation and curfews during the pandemic further exacerbate the mental health problem. Through this project, the researchers hoped to convey the negative impression of the pothole-strewn streets through an art installation. Using an artistic medium is one way to counteract the negative perception people have of potholes. In this project, freshly picked flowers, with only stems and no branches, were placed in the pothole. In this way, actual activity can be recorded based on the design of potholes. Keywords: Pothole, installation art. eISSN: 2398-4287© 2022. The Authors. Published for AMER ABRA cE-Bs by e-International Publishing House, Ltd., UK. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians/Africans/Arabians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DO

Determinants of the Passengers' Light Rail Transit Usage in the Klang Valley Malaysia

The study aims to examine the determinants that motivate passengers to use the Light Rail Transit in The Klang Valley, Malaysia. The factors identified in the study were traffic reduction, advantage of LRT, and attitude. This study used a questionnaire to gather the survey data. The convenience sampling technique was utilised in the study. SmartPLS Version 3 was used to test the hypotheses. The results of the study showed that traffic reduction, advantages of LRT and attitude have a positive influence towards the LRT usage. These significant variables can be used as a guide for the LRT operators to increase the LRT usage. Future research is suggested to include other potential factors that could stimulate more LRT usage and for it to become the preferred mode choice for travelling amongst the users

Quality of Service Practices within Business Market: An Automotive Industry Experience

In todays business market, products alone cannot assure vendors success due to the trend of the market where buyers perceived products and services are equally important. To face this current development, vendors are forced to offer added values to their products. Excellent quality of service is one of the options available because it can lead to the buyers satisfaction, and ultimately, loyalty. To date, little is known about the quality of service practices in the Malaysian Automotive Industry. Therefore, the case study was conducted to understand the practices better. It was investigated in the language of the actors in the industry specifically the buyers. In-depth interviews were conducted to explore the quality of service practices which were felt to be crucial to this study. A hermeneutics analysis method was employed in analyzing the data. Results from the case studies indicated that quality of service practices has played significant roles in the Malaysian Automotive Industry. In addition, the practices are consistent with the four main principles in a buyer-vendor relationship within the automotive industry. This is in line with the current market trend

Identifying scoliosis in population-based cohorts:automation of a validated method based on total body dual energy X-ray absorptiometry scans

Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes. However, to allow full utilisation of population-based research cohorts, this needs to be automated. The purpose of this study was therefore to automate the identification of spinal curvature from total body DXA scans using machine learning techniques. To validate the automation, we assessed: (1) sensitivity, specificity and area under the receiver operator curve value (AUC) by comparison with 12,000 manually annotated images; (2) reliability by rerunning the automation on a subset of DXA scans repeated 2–6 weeks apart and calculating the kappa statistic; (3) validity by applying the automation to 5000 non-annotated images to assess associations with epidemiological variables. The final automated model had a sensitivity of 86.5%, specificity of 96.9% and an AUC of 0.80 (95%CI 0.74–0.87). There was almost perfect agreement of identification of those with scoliosis (kappa 0.90). Those with scoliosis identified by the automated model showed similar associations with gender, ethnicity, socioeconomic status, BMI and lean mass to previous literature. In conclusion, we have developed an accurate and valid automated method for identifying and quantifying spinal curvature from total body DXA scans