title: Subjective cognitive complaints 8-Years after mild traumatic brain injury

authors: Duffy, Brittney

subjects: TBI

abstract: Mild traumatic brain injury (TBI) is common and can result in symptoms that persist for years after the initial injury. The symptoms following TBI are dynamic, and can result in cognitive, emotional, behavioural and physical symptoms, often collectively referred to as post-concussion symptoms. Persistent post-concussion symptoms impact education, employment, relationships and quality of life. The first aim of this study was to evaluate the long-term symptoms of adults eight- years after a mild TBI compared to a TBI-free group. The second aim of this study was to compare the prevalence and severity of post-concussion symptoms that were reported at 1-month post-injury, 12-months post-injury, and the symptoms reported now, at 8 years post-injury. Finally, the factors contributing to subjective cognitive complaints at 8-years post-injury were explored.
A population-based sample of 151 adults who sustained a mild TBI (mTBI) between 2010 and 2011 participated in this study. Additionally, 213 participants with no history of head injury took part. Both groups answered questions about current post-concussion symptoms using the Rivermead Post-Concussion Symptom Questionnaire (RPQ). The results of this study revealed the prevalence and severity of post- concussion symptoms was significantly greater in participants with mTBI at 8- years post-injury. Differences in cognitive symptoms were most prominent between groups, with the mTBI participants reporting significantly more cognitive complaints. When evaluating symptom-severity over time, we found no significant change in post-concussion symptoms from 12-months post-injury to 8-years. Older age at injury and increased symptoms of depression were associated with increased cognitive complaints at 8-years post-injury. These results confirm post-concussion symptoms persist long-term, and may not improve beyond the levels reported at 12-months post-injury. Further research is needed to explore the impact that treatment of mood symptoms early post- injury may have on recovery.

text: Subjective cognitive complaints 8-Years after mild traumatic brain injury Mild traumatic brain injury (TBI) is common and can result in symptoms that persist for years after the initial injury. The symptoms following TBI are dynamic, and can result in cognitive, emotional, behavioural and physical symptoms, often collectively referred to as post-concussion symptoms. Persistent post-concussion symptoms impact education, employment, relationships and quality of life. The first aim of this study was to evaluate the long-term symptoms of adults eight- years after a mild TBI compared to a TBI-free group. The second aim of this study was to compare the prevalence and severity of post-concussion symptoms that were reported at 1-month post-injury, 12-months post-injury, and the symptoms reported now, at 8 years post-injury. Finally, the factors contributing to subjective cognitive complaints at 8-years post-injury were explored.
A population-based sample of 151 adults who sustained a mild TBI (mTBI) between 2010 and 2011 participated in this study. Additionally, 213 participants with no history of head injury took part. Both groups answered questions about current post-concussion symptoms using the Rivermead Post-Concussion Symptom Questionnaire (RPQ). The results of this study revealed the prevalence and severity of post- concussion symptoms was significantly greater in participants with mTBI at 8- years post-injury. Differences in cognitive symptoms were most prominent between groups, with the mTBI participants reporting significantly more cognitive complaints. When evaluating symptom-severity over time, we found no significant change in post-concussion symptoms from 12-months post-injury to 8-years. Older age at injury and increased symptoms of depression were associated with increased cognitive complaints at 8-years post-injury. These results confirm post-concussion symptoms persist long-term, and may not improve beyond the levels reported at 12-months post-injury. Further research is needed to explore the impact that treatment of mood symptoms early post- injury may have on recovery.

year: 2019

title: Solutions to the unseen domain problem for machine learning-based glaucoma detection from retinal fundus images

authors: Gunasinghe, Hansi Nawam

abstract: Ocular diseases are a significant problem faced by 70 million people worldwide yearly. Identifying an eye disease requires a medical professional with years of specialist training. Glaucoma screening is often done manually, which is time-consuming. Automatic eye disease identification could be introduced to clinics to help speed up the detection and treatment of eye diseases. Retinal fundus images are helpful for glaucoma screening. However, the existing glaucoma identification systems trained using images from a specific camera fail to perform well with those captured from a new camera. This research aims to find solutions to address the above-unseen domain problem in machine learning-based glaucoma detection systems from retinal fundus images. The study was conducted in three phases, including an initial study that identifies the problem domain, followed by the manipulation of image preprocessing and image augmentation techniques to produce a more generalised glaucoma detection system.

In the first stage, twenty-eight pre-trained deep learning models for object recognition tasks were compared as potential feature extractors for glaucoma classification from retinal fundus images of the REFUGE dataset. First, the images were automatically cropped around the optic nerve head using a template-matching algorithm. Features were extracted using the pre-trained networks from both whole and cropped images. An extended feature set was created by concatenating those two feature sets. Finally, a ten-fold cross-validation experiment was conducted to compare the performance of random forest and logistic regression classifiers against each feature set. The best setup was when features were extracted from images using the ResNet101V2 ImageNet-pre-trained neural network and classified using a random forest classifier. However, the accuracy dropped when testing it with images from a new camera. Hence, the study was directed towards the unseen domain problem in the second stage.

In the study's second phase, transfer learning-based domain generalisation was applied together with multiple image preprocessing methods: input standardisation, median filtering and multi-image histogram matching for glaucoma detection using retinal fundus images from multiple cameras. The analysis included images from the RIMONEr2 and REFUGE datasets, which were captured using three camera models. A set of experiments were conducted using all possible combinations of training and testing camera devices, using the best system found in the first stage. Images were preprocessed in six different ways using either a single or a combination of three different preprocessing methods to see their effect on generalisation. The results indicated that the stylisation of test data might lead to better generalisation while reducing the retraining of an existing system. 

As a result, we compared multi-image histogram matching with neural style transferring to identify the classification accuracy during the testing phase of a model. We trained a random forest classifier and an XGBoost classifier with AlexNet and ResNet101V2 as feature extractors and tested the system following the same strategy as in phase two. Comparative results indicated that the neural style transferring better predicts the labels for unseen images. We continued experiments with neural style transferring to test publicly available models trained on the ACRIMA dataset. The method results better when reference images are selected from the same class. Given that the class information of real clinical data is unavailable, we suggest possible strategies for choosing better reference images. 

Overall, this study provides solutions to develop robust machine learning systems that require no retraining with new fundus cameras. The experimental results indicate that the proposed combination of preprocessing methods can be successfully utilised for better domain generalisation in the context of different retinal fundus camera devices. Furthermore, test-time data augmentation with neural style transferring leads to better predictions for images taken from unseen retinal fundus cameras. This reduces model retraining and increases the reusability of a pre-trained machine learning-based glaucoma detection system.

text: Solutions to the unseen domain problem for machine learning-based glaucoma detection from retinal fundus images Ocular diseases are a significant problem faced by 70 million people worldwide yearly. Identifying an eye disease requires a medical professional with years of specialist training. Glaucoma screening is often done manually, which is time-consuming. Automatic eye disease identification could be introduced to clinics to help speed up the detection and treatment of eye diseases. Retinal fundus images are helpful for glaucoma screening. However, the existing glaucoma identification systems trained using images from a specific camera fail to perform well with those captured from a new camera. This research aims to find solutions to address the above-unseen domain problem in machine learning-based glaucoma detection systems from retinal fundus images. The study was conducted in three phases, including an initial study that identifies the problem domain, followed by the manipulation of image preprocessing and image augmentation techniques to produce a more generalised glaucoma detection system.

In the first stage, twenty-eight pre-trained deep learning models for object recognition tasks were compared as potential feature extractors for glaucoma classification from retinal fundus images of the REFUGE dataset. First, the images were automatically cropped around the optic nerve head using a template-matching algorithm. Features were extracted using the pre-trained networks from both whole and cropped images. An extended feature set was created by concatenating those two feature sets. Finally, a ten-fold cross-validation experiment was conducted to compare the performance of random forest and logistic regression classifiers against each feature set. The best setup was when features were extracted from images using the ResNet101V2 ImageNet-pre-trained neural network and classified using a random forest classifier. However, the accuracy dropped when testing it with images from a new camera. Hence, the study was directed towards the unseen domain problem in the second stage.

In the study's second phase, transfer learning-based domain generalisation was applied together with multiple image preprocessing methods: input standardisation, median filtering and multi-image histogram matching for glaucoma detection using retinal fundus images from multiple cameras. The analysis included images from the RIMONEr2 and REFUGE datasets, which were captured using three camera models. A set of experiments were conducted using all possible combinations of training and testing camera devices, using the best system found in the first stage. Images were preprocessed in six different ways using either a single or a combination of three different preprocessing methods to see their effect on generalisation. The results indicated that the stylisation of test data might lead to better generalisation while reducing the retraining of an existing system. 

As a result, we compared multi-image histogram matching with neural style transferring to identify the classification accuracy during the testing phase of a model. We trained a random forest classifier and an XGBoost classifier with AlexNet and ResNet101V2 as feature extractors and tested the system following the same strategy as in phase two. Comparative results indicated that the neural style transferring better predicts the labels for unseen images. We continued experiments with neural style transferring to test publicly available models trained on the ACRIMA dataset. The method results better when reference images are selected from the same class. Given that the class information of real clinical data is unavailable, we suggest possible strategies for choosing better reference images. 

Overall, this study provides solutions to develop robust machine learning systems that require no retraining with new fundus cameras. The experimental results indicate that the proposed combination of preprocessing methods can be successfully utilised for better domain generalisation in the context of different retinal fundus camera devices. Furthermore, test-time data augmentation with neural style transferring leads to better predictions for images taken from unseen retinal fundus cameras. This reduces model retraining and increases the reusability of a pre-trained machine learning-based glaucoma detection system.

year: 2024

title: The "false generosity" of treaty settlements: innovation and contortion

authors: Te Aho, Linda

abstract: In Aotearoa New Zealand the ways in which indigenous claims to lands and waters are addressed are often looked to as good, or even best, practice by indigenous peoples around the world. While things are far from perfect, in recent decades Maori have succeeded in changing perceptions about the Treaty of Waitangi as the foundation of the nation, and work steadily continues on settling outstanding Treaty claims. The resulting Treaty of Waitangi settlements are negotiated arrangements which aim to remove a sense of historical grievance and achieve significant rebuilding of the Maori economy. They are seen by some as dynamic and powerful steps towards economic independence, as a means of recognising special relationships to lands and waters, and a necessary prerequisite to improved relationships between the state and the indigenous Maori in the future. ¹ Critics see the settlements and the processes followed to reach them as too heavily weighted in the government's favour. ² They argue that the settlements do not sufficiently compensate for actual losses. They are said to pit Maori against Maori. Diverse claimant groups are effectively forced to negotiate within standardised and fixed parameters. For these and other reasons, the settlement agreements, policies and processes have been labelled as divisive and compromising self-determination. ³

text: The "false generosity" of treaty settlements: innovation and contortion In Aotearoa New Zealand the ways in which indigenous claims to lands and waters are addressed are often looked to as good, or even best, practice by indigenous peoples around the world. While things are far from perfect, in recent decades Maori have succeeded in changing perceptions about the Treaty of Waitangi as the foundation of the nation, and work steadily continues on settling outstanding Treaty claims. The resulting Treaty of Waitangi settlements are negotiated arrangements which aim to remove a sense of historical grievance and achieve significant rebuilding of the Maori economy. They are seen by some as dynamic and powerful steps towards economic independence, as a means of recognising special relationships to lands and waters, and a necessary prerequisite to improved relationships between the state and the indigenous Maori in the future. ¹ Critics see the settlements and the processes followed to reach them as too heavily weighted in the government's favour. ² They argue that the settlements do not sufficiently compensate for actual losses. They are said to pit Maori against Maori. Diverse claimant groups are effectively forced to negotiate within standardised and fixed parameters. For these and other reasons, the settlement agreements, policies and processes have been labelled as divisive and compromising self-determination. ³

year: 2017