Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition.

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance

Transanal endoscopic operation (TEO) - local experience in a South African setting

Background: It is well recognised that the adenoma-carcinoma sequence is the mechanism by which most colorectal malignancies arise. Dysplastic adenomas are the precursor lesions which can progress to adenocarcinoma and premalignant sessile villous adenomas represent a particular challenge. Their early detection and removal can prevent rectal cancer. Local excision of low rectal tumors has become increasingly popular as technical advancement has rendered it easier and more effective. Local tumour excision avoids the complications of radical surgery. Transanal endoscopic operation (TEO) and Transanal endoscopic microsurgery (TEMS) are two equivalent techniques that have been widely adopted as the treatments of choice for large rectal adenomas and selected rectal cancers but has been under-employed in South Africa. The aim of this study was to evaluate TEO (the simpler and more affordable platform of the two) by describing the dimensions and anatomical parameters of specimens resected and using this to investigate whether any of these are predictive of recurrence, and to evaluate the incidence of complications of this less radical technique. Methods: In this single surgeon study, data was collected from pre-existing patient files (paper and electronic) during the first half of the time period and during the second half, was prospectively entered into a database. It includes all patients undergoing resection of benign and malignant rectal tumours by TEO at a private (Kingsbury Hospital) and public health institution (Groote Schuur Hospital) from January 2009 - May 2017. Electronic records, including operation notes, histology and radiology were reviewed. Results: Data was collected from January 2009 to May 2017. 110 patients in this study of which 87 (79.1%) were benign. There were 11 (12%) recurrences in this group. In the malignant group, there were 5 (21%) recurrences. The median tumour length was 4.5cm (IQR 2.5) and median tumour area was 16cm2 (IQR 20.11). For benign lesions, there was a significant difference in recurrence in patients presenting with incontinence (χ2 8.21, p-value<0.01, OR 16.7 (1.37-202.7)), lesions with involved surgical margins (χ2 6.29 p-value 0.01, OR 6.75 (95% CI 1.02 - 35.7)) and circumferential tumours (χ2 6.31 p-value 0.04, 6.5 (1.17-36.3)). The multinomial logistic regression model for benign lesions revealed that only incontinence and involved surgical margins were independent predictors of recurrence. Complications occurred in 21 (19.1%) patients with circumferential lesions, length of the tumour, and malignancy being predictive of complications. Conclusion: This study constitutes the only report of TEO or TEMS from a low- or middle-income country (LMIC). The results are in keeping with the published literature, demonstrating its safety and feasibility in a LMIC setting, which will reduce the need for expensive, highly morbid radical surgery for benign and malignant disease. The recommendation is for a wider introduction of TEO in South Africa and other LMIC countries with the provision of adequate training

Recurrent Convolutional Neural Networks: A Better Model of Biological Object Recognition

Feedforward neural networks provide the dominant model of how the brain performs visual object recognition. However, these networks lack the lateral and feedback connections, and the resulting recurrent neuronal dynamics, of the ventral visual pathway in the human and non-human primate brain. Here we investigate recurrent convolutional neural networks with bottom-up (B), lateral (L), and top-down (T) connections. Combining these types of connections yields four architectures (B, BT, BL, and BLT), which we systematically test and compare. We hypothesized that recurrent dynamics might improve recognition performance in the challenging scenario of partial occlusion. We introduce two novel occluded object recognition tasks to test the efficacy of the models, digit clutter (where multiple target digits occlude one another) and digit debris (where target digits are occluded by digit fragments). We find that recurrent neural networks outperform feedforward control models (approximately matched in parametric complexity) at recognizing objects, both in the absence of occlusion and in all occlusion conditions. Recurrent networks were also found to be more robust to the inclusion of additive Gaussian noise. Recurrent neural networks are better in two respects: (1) they are more neurobiologically realistic than their feedforward counterparts; (2) they are better in terms of their ability to recognize objects, especially under challenging conditions. This work shows that computer vision can benefit from using recurrent convolutional architectures and suggests that the ubiquitous recurrent connections in biological brains are essential for task performance

The Economics of Vivax Malaria Treatment

The control and eventual elimination of malaria will require widescale adoption of strategies to ensure early diagnosis and highly effective treatment of infected individuals. In Plasmodium vivax, multiple relapse episodes can only be averted by treating the dormant liver stage of the parasite life cycle - a strategy known as radical cure. This thesis aims to investigate key factors determining treatment-seeking behaviour and costs associated with vivax malaria, how these influence healthcare decisions, the cost-effectiveness of screening tests and treatments for radical cure, and the cost-benefit implications of their global implementation. Treatment-seeking behaviour, assessed in Papua, Indonesia, demonstrated that household costs per person seeking treatment for vivax malaria were similar to those for falciparum malaria. Switching from ineffective to effective malaria treatment in the public sector improved the behaviour of patients and both public and private healthcare providers. Diagnostic strategies were investigated on the Thai-Myanmar border, where screening for glucose-6-phosphate-dehydrogenase (G6PD) deficiency prior to radical cure reduced the disease burden while being a cost-effective option for healthcare providers that do not use radical cure and a potentially cost-saving alternative where primaquine is prescribed without screening. The costs of implementing G6PD screening were collected in three countries, and highlighted that RDTs consistently reduced costs as compared to the widely-used fluorescent spot test. Across four countries, the indirect cost due to lost productivity was the largest cost component for households of patients with vivax malaria. After combining provider and patient costs with case estimates, the global economic burden of vivax malaria was estimated to be US$330 million per year. Adopting a policy of screening for G6PD deficiency with delivery of highly effective radical cure was projected to save US$45 million. P. vivax malaria causes a large economic burden that can be reduced substantially by delivering safe and effective radical cure

A Gift of Nature and the Source of Violent Conflict: Land and Boundary Disputes in the North West Region of Cameroon The Case of BaliKumbat and Bafanji

Balikumbat and Bafanji are the names of two villages in the Northwest Region of Cameroon that have been warring against one another over Bangang, a tract of fertile land. The conflict hinges on perceived differences about who should have access to this fertile land. Both villages claim ownership. This conflict has persisted from colonial times to the present with no tangible resolution. Understanding the place of land within the political, social, and economic fabric of the lives of both villages prior to and after the arrival of the colonial administration is the centerpiece of this research endeavor. This study sheds light on why the conflict persists. The land tenure decree of 1973, which was later promulgated into Cameroon law in 1984, is the most recent attempt at resolving disputes over land. It did not resolve this conflict. A clash of cultures between the indigenous population and the European colonizers may have triggered a legacy of land conflict between these two communities. This study unravels and seeks to explain when the Balikumbat and Bafanji villages transitioned from being two loving neighbors, capable of sharing their use of and kinship to the land, to hostile enemies ready to fight and kill one another at the earliest opportunity. In this study, interviews, observations, journal intakes, field notes, as well as document reviews, are pivotal tools used in justifying the claims highlighted in the research

Biologically-inspired Neural Networks for Shape and Color Representation

The goal of human-level performance in artificial vision systems is yet to be achieved. With this goal, a reasonable choice is to simulate this biological system with computational models that mimic its visual processing. A complication with this approach is that the human brain, and similarly its visual system, are not fully understood. On the bright side, with remarkable findings in the field of visual neuroscience, many questions about visual processing in the primate brain have been answered in the past few decades. Nonetheless, a lag in incorporating these new discoveries into biologically-inspired systems is evident. The present work introduces novel biologically-inspired models that employ new findings of shape and color processing into analytically-defined neural networks. In contrast to most current methods that attempt to learn all aspects of behavior from data, here we propose to bootstrap such learning by building upon existing knowledge rather than learning from scratch. Put simply, the processing networks are defined analytically using current neural understanding and learned where such knowledge is not available. This is thus a hybrid strategy that hopefully combines the best of both worlds. Experiments on the artificial neurons in the proposed networks demonstrate that these neurons mimic the studied behavior of biological cells, suggesting a path forward for incorporating analytically-defined artificial neural networks into computer vision systems