title: Off-line signature verification

authors: Larkins, Robert L.

subjects: off-line signature verification

abstract: In today’s society signatures are the most accepted form of identity verification. However, they have the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. This thesis implements and tests current approaches to off-line signature verification with the goal of determining the most beneficial techniques that are available. This investigation will also introduce novel techniques that are shown to significantly boost the achieved classification accuracy for both person-dependent (one-class training) and person-independent (two-class training) signature verification learning strategies. The findings presented in this thesis show that many common techniques do not always give any significant advantage and in some cases they actually detract from the classification accuracy. Using the techniques that are proven to be most beneficial, an effective approach to signature verification is constructed, which achieves approximately 90% and 91% on the standard CEDAR and GPDS signature datasets respectively. These results are significantly better than the majority of results that have been previously published. Additionally, this approach is shown to remain relatively stable when a minimal number of training signatures are used, representing feasibility for real-world situations.

text: Off-line signature verification In today’s society signatures are the most accepted form of identity verification. However, they have the unfortunate side-effect of being easily abused by those who would feign the identification or intent of an individual. This thesis implements and tests current approaches to off-line signature verification with the goal of determining the most beneficial techniques that are available. This investigation will also introduce novel techniques that are shown to significantly boost the achieved classification accuracy for both person-dependent (one-class training) and person-independent (two-class training) signature verification learning strategies. The findings presented in this thesis show that many common techniques do not always give any significant advantage and in some cases they actually detract from the classification accuracy. Using the techniques that are proven to be most beneficial, an effective approach to signature verification is constructed, which achieves approximately 90% and 91% on the standard CEDAR and GPDS signature datasets respectively. These results are significantly better than the majority of results that have been previously published. Additionally, this approach is shown to remain relatively stable when a minimal number of training signatures are used, representing feasibility for real-world situations.

year: 2009

title: A comparison of machine learning methods for cross-domain few-shot learning

authors: Wang, Hongyu

subjects: computer science

abstract: We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

text: A comparison of machine learning methods for cross-domain few-shot learning We present an empirical evaluation of machine learning algorithms in cross-domain few-shot learning based on a fixed pre-trained feature extractor. Experiments were performed in five target domains (CropDisease, EuroSAT, Food101, ISIC and ChestX) and using two feature extractors: a ResNet10 model trained on a subset of ImageNet known as miniImageNet and a ResNet152 model trained on the ILSVRC 2012 subset of ImageNet. Commonly used machine learning algorithms including logistic regression, support vector machines, random forests, nearest neighbour classification, naïve Bayes, and linear and quadratic discriminant analysis were evaluated on the extracted feature vectors. We also evaluated classification accuracy when subjecting the feature vectors to normalisation using p-norms. Algorithms originally developed for the classification of gene expression data—the nearest shrunken centroid algorithm and LDA ensembles obtained with random projections—were also included in the experiments, in addition to a cosine similarity classifier that has recently proved popular in few-shot learning. The results enable us to identify algorithms, normalisation methods and pre-trained feature extractors that perform well in cross-domain few-shot learning. We show that the cosine similarity classifier and   ℓ² -regularised 1-vs-rest logistic regression are generally the best-performing algorithms. We also show that algorithms such as LDA yield consistently higher accuracy when applied to   ℓ² -normalised feature vectors. In addition, all classifiers generally perform better when extracting feature vectors using the ResNet152 model instead of the ResNet10 model.

year: 2020

title: Patient-reported diagnostic intervals to colorectal cancer diagnosis in the Midland region of New Zealand: a prospective cohort study

authors: Blackmore, Tania Louise

subjects: Science & Technology

abstract: Background and objectives
New Zealand (NZ) has high rates of colorectal cancer (CRC) but low rates of early detection. The majority of CRC is diagnosed through general practice, where lengthy diagnostic intervals are common. We investigated factors contributing to diagnostic delay in a cohort of patients newly diagnosed with CRC.

Methods
Patients were recruited from the Midland region and interviewed about their diagnostic experience using a questionnaire based on a modified Model of Pathways to Treatment framework and SYMPTOM questionnaire. Descriptive statistics were used to describe the population characteristics. Chi-square analysis and logistic regression were used to analyse factors influencing diagnostic intervals.

Results
Data from 176 patients were analysed, of which 65 (36.9%) experienced a general practitioner (GP) diagnostic interval of >120 days and 96 (54.5%) experienced a total diagnostic interval (TDI) > 120 days. Patients reporting rectal bleeding were less likely to experience a long TDI (odds ratio [OR] 0.34, 95% confidence interval [CI]: 0.14–0.78) and appraisal/help-seeking interval (OR, 0.19, 95% CI: 0.06–0.59). Patients <60 were more likely to report a longer appraisal/help-seeking interval (OR, 3.32, 95% CI: 1.17–9.46). Female (OR, 2.19, 95% CI: 1.08–4.44) and Māori patients (OR, 3.18, 95% CI: 1.04–9.78) were more likely to experience a long GP diagnostic interval.

Conclusion
NZ patients with CRC can experience long diagnostic intervals, attributed to patient and health system factors. Young patients, Māori, females, and patients experiencing change of bowel habit may be at particular risk. We need to increase symptom awareness of CRC for patients and GPs. Concentrated efforts are needed to ensure equity for Māori in access to screening, diagnostics, and treatment.

text: Patient-reported diagnostic intervals to colorectal cancer diagnosis in the Midland region of New Zealand: a prospective cohort study Background and objectives
New Zealand (NZ) has high rates of colorectal cancer (CRC) but low rates of early detection. The majority of CRC is diagnosed through general practice, where lengthy diagnostic intervals are common. We investigated factors contributing to diagnostic delay in a cohort of patients newly diagnosed with CRC.

Methods
Patients were recruited from the Midland region and interviewed about their diagnostic experience using a questionnaire based on a modified Model of Pathways to Treatment framework and SYMPTOM questionnaire. Descriptive statistics were used to describe the population characteristics. Chi-square analysis and logistic regression were used to analyse factors influencing diagnostic intervals.

Results
Data from 176 patients were analysed, of which 65 (36.9%) experienced a general practitioner (GP) diagnostic interval of >120 days and 96 (54.5%) experienced a total diagnostic interval (TDI) > 120 days. Patients reporting rectal bleeding were less likely to experience a long TDI (odds ratio [OR] 0.34, 95% confidence interval [CI]: 0.14–0.78) and appraisal/help-seeking interval (OR, 0.19, 95% CI: 0.06–0.59). Patients <60 were more likely to report a longer appraisal/help-seeking interval (OR, 3.32, 95% CI: 1.17–9.46). Female (OR, 2.19, 95% CI: 1.08–4.44) and Māori patients (OR, 3.18, 95% CI: 1.04–9.78) were more likely to experience a long GP diagnostic interval.

Conclusion
NZ patients with CRC can experience long diagnostic intervals, attributed to patient and health system factors. Young patients, Māori, females, and patients experiencing change of bowel habit may be at particular risk. We need to increase symptom awareness of CRC for patients and GPs. Concentrated efforts are needed to ensure equity for Māori in access to screening, diagnostics, and treatment.

year: 2021