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 challenges of teacher education-exploiting synergies to meet multiple demands

authors: Cooper, Beverley

abstract: Internationally questions about the nature of effective teacher education have come to the fore as a consequence of the demands to produce equitable outcomes from a more diverse range of learners working with ever more challenging curriculum. This expansion has arisen in part from a realisation that mastery of formal conceptual or propositional knowledge alone is no longer a sufficient basis for active participation in today’s knowledge society. In conjunction with these expectations and understandings, policy makers have introduced a range of mechanisms to hold teachers, schools, and teacher educators to account for student learning and achievement. These mechanisms include the specification of standards for either or both of student learning and teacher performance, along with a requirement to provide evidence of impact. University-based teacher educators are in unique positions of being accountable to students, teachers and teacher accreditation agencies or teacher learning and development that impacts positively for students in the classroom, and being accountable to their university in terms of research productivity. They also need to ensure student teacher graduates develop the attributes specified in their university graduate profile.
In this paper, we use New Zealand as a case study to explore some of the convergences that teacher educators might usefully consider across school curriculum goals, university graduate attributes, and teacher performance standards for preservice and practicing teachers, where these have the potential to shape and inform their practice and learning. We begin by elaborating on the landscape of change and then provide two examples of convergence from our own work and that of our colleagues. We describe how projects we have conducted assist in meeting multiple goals. Firstly we illustrate how the need to develop numerate citizens can be imbedded in a teacher education program and secondly illustrate how the need to develop the capacity for learning lifelong can be developed through researched program innovations based on a combination of a design research approach and a design-based intervention research approach. Drawing on our analysis, we scope some of the possibilities for exploiting synergies these shifts can have for initial teacher education and teacher educators.

text: The challenges of teacher education-exploiting synergies to meet multiple demands Internationally questions about the nature of effective teacher education have come to the fore as a consequence of the demands to produce equitable outcomes from a more diverse range of learners working with ever more challenging curriculum. This expansion has arisen in part from a realisation that mastery of formal conceptual or propositional knowledge alone is no longer a sufficient basis for active participation in today’s knowledge society. In conjunction with these expectations and understandings, policy makers have introduced a range of mechanisms to hold teachers, schools, and teacher educators to account for student learning and achievement. These mechanisms include the specification of standards for either or both of student learning and teacher performance, along with a requirement to provide evidence of impact. University-based teacher educators are in unique positions of being accountable to students, teachers and teacher accreditation agencies or teacher learning and development that impacts positively for students in the classroom, and being accountable to their university in terms of research productivity. They also need to ensure student teacher graduates develop the attributes specified in their university graduate profile.
In this paper, we use New Zealand as a case study to explore some of the convergences that teacher educators might usefully consider across school curriculum goals, university graduate attributes, and teacher performance standards for preservice and practicing teachers, where these have the potential to shape and inform their practice and learning. We begin by elaborating on the landscape of change and then provide two examples of convergence from our own work and that of our colleagues. We describe how projects we have conducted assist in meeting multiple goals. Firstly we illustrate how the need to develop numerate citizens can be imbedded in a teacher education program and secondly illustrate how the need to develop the capacity for learning lifelong can be developed through researched program innovations based on a combination of a design research approach and a design-based intervention research approach. Drawing on our analysis, we scope some of the possibilities for exploiting synergies these shifts can have for initial teacher education and teacher educators.

year: 2019

title: Angitūtanga o ngā tauira Māori:  Supporting Māori students to succeed in psychology

authors: Martin, Dionne

subjects: Psychology

abstract: Many Māori seek out university degrees to make a meaningful impact on the health of their community. Psychology, is one avenue that attracts Māori to do so. Psychology training has often failed to meet the needs of Māori students, often creating barriers to Māori student success. This thesis will explore barriers and support mechanisms that Māori students experience while attending university in pursuit of psychology degrees. This study will focus on experiences had by those enrolled in The School of Psychology at The Univeristy of Waikato, on the Hamilton campus. Informed by kaupapa Māori guidelines and analysed though the use of pūrākau this thesis highlights some key experiences shared by Māori psychology students. Three key experiences were identified. First, the prominence of Western culture in The School of psychology and the impact that has on Māori sense of belonging and whanaungatanga. Second, support system realities for Māori, identifying key university supports systems and realities associated with ones home environment. Third, the transitional journey to becoming a psychologist and what impact that has on the university journey. With this in mind, some recommendations are made to support Māori student success in psychology. These recommendations advocate for the provision of a more bicultural learning experience, creation of a culturally affirming environment, recruitment and retention of culturally capable staff, appropriate resourcing, funding and training for support services, better support for Māori realities and pathway navigation support.

text: Angitūtanga o ngā tauira Māori:  Supporting Māori students to succeed in psychology Many Māori seek out university degrees to make a meaningful impact on the health of their community. Psychology, is one avenue that attracts Māori to do so. Psychology training has often failed to meet the needs of Māori students, often creating barriers to Māori student success. This thesis will explore barriers and support mechanisms that Māori students experience while attending university in pursuit of psychology degrees. This study will focus on experiences had by those enrolled in The School of Psychology at The Univeristy of Waikato, on the Hamilton campus. Informed by kaupapa Māori guidelines and analysed though the use of pūrākau this thesis highlights some key experiences shared by Māori psychology students. Three key experiences were identified. First, the prominence of Western culture in The School of psychology and the impact that has on Māori sense of belonging and whanaungatanga. Second, support system realities for Māori, identifying key university supports systems and realities associated with ones home environment. Third, the transitional journey to becoming a psychologist and what impact that has on the university journey. With this in mind, some recommendations are made to support Māori student success in psychology. These recommendations advocate for the provision of a more bicultural learning experience, creation of a culturally affirming environment, recruitment and retention of culturally capable staff, appropriate resourcing, funding and training for support services, better support for Māori realities and pathway navigation support.

year: 2025