title: Deep learning in diabetic foot ulcers detection: A comprehensive evaluation

authors: Yap, Moi Hoon

subjects: computer science

abstract: There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

text: Deep learning in diabetic foot ulcers detection: A comprehensive evaluation There has been a substantial amount of research involving computer methods and technology for the detection and recognition of diabetic foot ulcers (DFUs), but there is a lack of systematic comparisons of state-of-the-art deep learning object detection frameworks applied to this problem. DFUC2020 provided participants with a comprehensive dataset consisting of 2,000 images for training and 2,000 images for testing. This paper summarizes the results of DFUC2020 by comparing the deep learning-based algorithms proposed by the winning teams: Faster R–CNN, three variants of Faster R–CNN and an ensemble method; YOLOv3; YOLOv5; EfficientDet; and a new Cascade Attention Network. For each deep learning method, we provide a detailed description of model architecture, parameter settings for training and additional stages including pre-processing, data augmentation and post-processing. We provide a comprehensive evaluation for each method. All the methods required a data augmentation stage to increase the number of images available for training and a post-processing stage to remove false positives. The best performance was obtained from Deformable Convolution, a variant of Faster R–CNN, with a mean average precision (mAP) of 0.6940 and an F1-Score of 0.7434. Finally, we demonstrate that the ensemble method based on different deep learning methods can enhance the F1-Score but not the mAP.

year: 2021

title: Key competency development and students use of digital learning objects

authors: Falloon, Garry

subjects: competencies

abstract: The inclusion of key competencies in the New Zealand Curriculum (2007) has presented challenges for teachers in their efforts to gather evidence and detail student progress for reporting purposes. Research identifies the need to adopt different evaluation processes and systems, as outcomes and progression in key competencies is fundamentally different from those associated with more conventional learning. It also suggests the use of digital tools may assist in this process, but offers few suggestions as to how this might take place. This article introduces and describes a current research project utilising a thinking skills framework and screen-recording software to map students’ interaction with digital learning objects, and explore the extent to which they provide opportunities to develop thinking and relating to others competencies. It suggests the approach offers potential to make explicit for reporting purposes the nature and quality of students’ thinking, and how their interaction with others in groups, influences their ability to solve problems presented by the objects. However, it also suggests the approach may suffer from manageability challenges, and that student-led administration systems need to be developed to ensure its viability in whole class contexts

text: Key competency development and students use of digital learning objects The inclusion of key competencies in the New Zealand Curriculum (2007) has presented challenges for teachers in their efforts to gather evidence and detail student progress for reporting purposes. Research identifies the need to adopt different evaluation processes and systems, as outcomes and progression in key competencies is fundamentally different from those associated with more conventional learning. It also suggests the use of digital tools may assist in this process, but offers few suggestions as to how this might take place. This article introduces and describes a current research project utilising a thinking skills framework and screen-recording software to map students’ interaction with digital learning objects, and explore the extent to which they provide opportunities to develop thinking and relating to others competencies. It suggests the approach offers potential to make explicit for reporting purposes the nature and quality of students’ thinking, and how their interaction with others in groups, influences their ability to solve problems presented by the objects. However, it also suggests the approach may suffer from manageability challenges, and that student-led administration systems need to be developed to ensure its viability in whole class contexts

year: 2012

title: Examining the potential of critical and Kaupapa Māori approaches to leading education reform in New Zealand’s English-medium secondary schools

authors: Berryman, Mere

abstract: This paper discusses expectations, policies and practices that currently underpin education within the New Zealand context. It acknowledges the ongoing failure of this policy framework to positively influence reform for Indigenous Māori students in regular, state-funded schools and highlights the need for extensive change in the positioning and expectations of educators if Māori learners are to realize their true potential. The paper then considers leadership models to reimagine and lead a transformative educational reform that aims to include the aspirations and contributions of all members of the school’s communities, especially those who have historically been marginalized. Finally it considers the implications of this model for international application.

text: Examining the potential of critical and Kaupapa Māori approaches to leading education reform in New Zealand’s English-medium secondary schools This paper discusses expectations, policies and practices that currently underpin education within the New Zealand context. It acknowledges the ongoing failure of this policy framework to positively influence reform for Indigenous Māori students in regular, state-funded schools and highlights the need for extensive change in the positioning and expectations of educators if Māori learners are to realize their true potential. The paper then considers leadership models to reimagine and lead a transformative educational reform that aims to include the aspirations and contributions of all members of the school’s communities, especially those who have historically been marginalized. Finally it considers the implications of this model for international application.

year: 2016