Evaluating retraining rules for semi-supervised learning in neural network based cursive word recognition

Training a system to recognize handwritten words is a task that requires a large amount of data with their correct transcription. However, the creation of such a training set, including the generation of the ground truth, is tedious and costly. One way of reducing the high cost of labeled training data acquisition is to exploit unlabeled data, which can be gathered easily. Making use of both labeled and unlabeled data is known as semi-supervised learning. One of the most general versions of semi-supervised learning is self-training, where a recognizer iteratively retrains itself on its own output on new, unlabeled data. In this paper we propose to apply semi-supervised learning, and in particular self-training, to the problem of cursive, handwritten word recognition. The special focus of the paper is on retraining rules that define what data are actually being used in the retraining phase. In a series of experiments it is shown that the performance of a neural network based recognizer can be significantly improved through the use of unlabeled data and self-training if appropriate retraining rules are applied.

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient-specific semi-supervised learning. The idea behind our semi-supervised approach is to effectively fuse information from both pre- and postoperative image data of the same patient to improve segmentation of the postoperative image. We pose image segmentation as a classification problem and solve it by adopting a semi-supervised decision forest. The method is evaluated on a cohort of 10 high-grade glioma patients, with segmentation performance and computation time comparable or superior to a state-of-the-art brain tumor segmentation method. Moreover, our results confirm that the inclusion of preoperative MR images lead to a better performance regarding postoperative brain tumor segmentation.

Facial Nerve Image Enhancement from CBCT using Supervised Learning Technique

Facial nerve segmentation plays an important role in surgical planning of cochlear implantation. Clinically available CBCT images are used for surgical planning. However, its relatively low resolution renders the identification of the facial nerve difficult. In this work, we present a supervised learning approach to enhance facial nerve image information from CBCT. A supervised learning approach based on multi-output random forest was employed to learn the mapping between CBCT and micro-CT images. Evaluation was performed qualitatively and quantitatively by using the predicted image as input for a previously published dedicated facial nerve segmentation, and cochlear implantation surgical planning software, OtoPlan. Results show the potential of the proposed approach to improve facial nerve image quality as imaged by CBCT and to leverage its segmentation using OtoPlan.

Prediction of Trabecular Bone Anisotropy from Quantitative Computed Tomography using Supervised Learning and a Novel Morphometric Feature Descriptor

Patient-specific biomechanical models including local bone mineral density and anisotropy have gained importance for assessing musculoskeletal disorders. However the trabecular bone anisotropy captured by high-resolution imaging is only available at the peripheral skeleton in clinical practice. In this work, we propose a supervised learning approach to predict trabecular bone anisotropy that builds on a novel set of pose invariant feature descriptors. The statistical relationship between trabecular bone anisotropy and feature descriptors were learned from a database of pairs of high resolution QCT and clinical QCT reconstructions. On a set of leave-one-out experiments, we compared the accuracy of the proposed approach to previous ones, and report a mean prediction error of 6% for the tensor norm, 6% for the degree of anisotropy and 19◦ for the principal tensor direction. These findings show the potential of the proposed approach to predict trabecular bone anisotropy from clinically available QCT images.

Fast Prediction of Femoral Biomechanics Using Supervised Machine Learning and Statistical Shape Modeling

Finite element (FE) analysis is an important computational tool in biomechanics. However, its adoption into clinical practice has been hampered by its computational complexity and required high technical competences for clinicians. In this paper we propose a supervised learning approach to predict the outcome of the FE analysis. We demonstrate our approach on clinical CT and X-ray femur images for FE predictions ( FEP), with features extracted, respectively, from a statistical shape model and from 2D-based morphometric and density information. Using leave-one-out experiments and sensitivity analysis, comprising a database of 89 clinical cases, our method is capable of predicting the distribution of stress values for a walking loading condition with an average correlation coefficient of 0.984 and 0.976, for CT and X-ray images, respectively. These findings suggest that supervised learning approaches have the potential to leverage the clinical integration of mechanical simulations for the treatment of musculoskeletal conditions.

Learning by the Dendritic Prediction of Somatic Spiking

Recent modeling of spike-timing-dependent plasticity indicates that plasticity involves as a third factor a local dendritic potential, besides pre- and postsynaptic firing times. We present a simple compartmental neuron model together with a non-Hebbian, biologically plausible learning rule for dendritic synapses where plasticity is modulated by these three factors. In functional terms, the rule seeks to minimize discrepancies between somatic firings and a local dendritic potential. Such prediction errors can arise in our model from stochastic fluctuations as well as from synaptic input, which directly targets the soma. Depending on the nature of this direct input, our plasticity rule subserves supervised or unsupervised learning. When a reward signal modulates the learning rate, reinforcement learning results. Hence a single plasticity rule supports diverse learning paradigms.

An innovative blended learning approach using virtual patients as preparation for skills laboratory training: perceptions of students and tutors

BACKGROUND Currently only a few reports exist on how to prepare medical students for skills laboratory training. We investigated how students and tutors perceive a blended learning approach using virtual patients (VPs) as preparation for skills training. METHODS Fifth-year medical students (N=617) were invited to voluntarily participate in a paediatric skills laboratory with four specially designed VPs as preparation. The cases focused on procedures in the laboratory using interactive questions, static and interactive images, and video clips. All students were asked to assess the VP design. After participating in the skills laboratory 310 of the 617 students were additionally asked to assess the blended learning approach through established questionnaires. Tutors' perceptions (N=9) were assessed by semi-structured interviews. RESULTS From the 617 students 1,459 VP design questionnaires were returned (59.1%). Of the 310 students 213 chose to participate in the skills laboratory; 179 blended learning questionnaires were returned (84.0%). Students provided high overall acceptance ratings of the VP design and blended learning approach. By using VPs as preparation, skills laboratory time was felt to be used more effectively. Tutors perceived students as being well prepared for the skills laboratory with efficient uses of time. CONCLUSION The overall acceptance of the blended learning approach was high among students and tutors. VPs proved to be a convenient cognitive preparation tool for skills training.

Forget about theory - practice is all? - Student teachers' learning in practicum

In 1999, all student teachers at secondary I level at the University of Bern who had to undertake an internship were asked to participate in a study on learning processes during practicum: 150 students and their mentors in three types of practicum participated—introductory practicum (after the first half‐year of studies), intermediate practicum (after two years of studies) and final practicum (after three years of studies). At the end of the practicum, student teachers and mentors completed questionnaires on preparing, teaching and post‐processing lessons. All student teachers, additionally, rated their professional skills and aspects of personality (attitudes towards pupils, self‐assuredness and well‐being) before and after the practicum. Forty‐six student teachers wrote daily semi‐structured diaries about essential learning situations during their practicum. Results indicate that in each practicum students improved significantly in preparing, conducting and post‐processing lessons. The mentors rated these changes as being greater than did the student teachers. From the perspective of the student teachers their general teaching skills also improved, and their attitudes toward pupils became more open. Furthermore, during practicum their self‐esteem and subjective well‐being increased. Diary data confirmed that there are no differences between different levels of practicum in terms of learning outcomes, but give some first insight into different ways of learning during internship.

Livelihood and Community Development: LforS learning and simulation game

Up to 15 people can participate in the game, which is supervised by a moderator. Households consisting of 1-5 people discuss options for diversification of household strategies. Aim of the game: By devising appropriate strategies, households seek to stand up to various types of events while improving their economic and social situation and, at the same time, taking account of ecological conditions. The annual General Community Meeting (GCM) provides an opportunity for households to create a general set-up at the local level that is more or less favourable to the strategies they are pursuing. The development of a community investment strategy, to be implemented by the GCM, and successful coordination between households will allow players to optimise their investments at the household level. The household who owns the most assets at the end of the game wins. Players participate very actively, as the game stimulates lively and interesting discussions. They find themselves confronted with different types of decision-making related to the reality of their daily lives. They explore different ways to model their own household strategies and discuss risks and opportunities. Reflections on the course of the game continually refer to the real-life situations of the participants.

Learning through Listening: Conversations for Strategic Change in a Healtcare Provider

The aim of this paper is investigate the role of conversation in strategic change so as to enhance both theory and practice in this respect. As an investigation on how conversations shape change processes in practice, we reflect on an interpretive case study in a health care organization. Through an OD project complemented by semi-structured interviews with participants, we gained a set of data and experiences that allows us to inquire into the relationship between conversations and change in more depth.