To determine the appropriate treatment plan for patients, radiologists must reliably detect brain tumors. Despite the fact that manual segmentation involves a great deal of knowledge and ability, it may sometimes be inaccurate. By evaluating the size, location, structure, and grade of the tumor, automatic tumor segmentation in MRI images aids in a more thorough analysis of pathological conditions. Due to the intensity differences in MRI images, gliomas may spread out, have low contrast, and are therefore difficult to detect. As a result, segmenting brain tumors is a challenging process. In the past, several methods for segmenting brain tumors in MRI scans were created. However, because of their susceptibility to noise and distortions, the usefulness of these approaches is limited. Self-Supervised Wavele- based Attention Network (SSW-AN), a new attention module with adjustable self-supervised activation functions and dynamic weights, is what we suggest as a way to collect global context information. In particular, this network's input and labels are made up of four parameters produced by the two-dimensional (2D) Wavelet transform, which makes the training process simpler by neatly segmenting the data into low-frequency and high-frequency channels. To be more precise, we make use of the channel attention and spatial attention modules of the self-supervised attention block (SSAB). As a result, this method may more easily zero in on crucial underlying channels and spatial patterns. The suggested SSW-AN has been shown to outperform the current state-of-the-art algorithms in medical image segmentation tasks, with more accuracy, more promising dependability, and less unnecessary redundancy.

• Klíčová slova
• Wavelet transform, attention mechanisms, self-supervised attention block (SSAB), self-supervised wavelet-based attention network (SSW-AN), semantic image segmentation,
• MeSH
• algoritmy MeSH
• lidé MeSH
• magnetická rezonanční tomografie metody   MeSH
• nádory mozku * MeSH
• počítačové zpracování obrazu metody   MeSH
• sémantika * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

The performance of deep neural networks and the low costs of computational hardware has made computer vision a popular choice in many robotic systems. An attractive feature of deep-learned methods is their ability to cope with appearance changes caused by day-night cycles and seasonal variations. However, deep learning of neural networks typically relies on large numbers of hand-annotated images, which requires significant effort for data collection and annotation. We present a method that allows autonomous, self-supervised training of a neural network in visual teach-and-repeat (VT&R) tasks, where a mobile robot has to traverse a previously taught path repeatedly. Our method is based on a fusion of two image registration schemes: one based on a Siamese neural network and another on point-feature matching. As the robot traverses the taught paths, it uses the results of feature-based matching to train the neural network, which, in turn, provides coarse registration estimates to the feature matcher. We show that as the neural network gets trained, the accuracy and robustness of the navigation increases, making the robot capable of dealing with significant changes in the environment. This method can significantly reduce the data annotation efforts when designing new robotic systems or introducing robots into new environments. Moreover, the method provides annotated datasets that can be deployed in other navigation systems. To promote the reproducibility of the research presented herein, we provide our datasets, codes and trained models online.

• Klíčová slova
• artificial neural network, computer vision, deep learning, long-term autonomy, mobile robot, self-supervised machine learning, visual teach and repeat navigation,
• MeSH
• datové kurátorství MeSH
• neuronové sítě * MeSH
• reprodukovatelnost výsledků MeSH
• ruka * MeSH
• výzkumný projekt MeSH
• Publikační typ
• časopisecké články MeSH

In this paper, a novel U-Net-based method for robust adherent cell segmentation for quantitative phase microscopy image is designed and optimised. We designed and evaluated four specific post-processing pipelines. To increase the transferability to different cell types, non-deep learning transfer with adjustable parameters is used in the post-processing step. Additionally, we proposed a self-supervised pretraining technique using nonlabelled data, which is trained to reconstruct multiple image distortions and improved the segmentation performance from 0.67 to 0.70 of object-wise intersection over union. Moreover, we publish a new dataset of manually labelled images suitable for this task together with the unlabelled data for self-supervised pretraining.

• Publikační typ
• časopisecké články MeSH

Recent research has demonstrated the significance of incorporating invariance into neural networks. However, existing methods require direct sampling over the entire transformation set, notably computationally taxing for large groups like the affine group. In this study, we propose a more efficient approach by addressing the invariances of the subgroups within a larger group. For tackling affine invariance, we split it into the Euclidean group E(n) and uni-axial scaling group US(n), handling invariance individually. We employ an E(n)-invariant model for E(n)-invariance and average model outputs over data augmented from a US(n) distribution for US(n)-invariance. Our method maintains a favorable computational complexity of O(N2) in 2D and O(N4) in 3D scenarios, in contrast to the O(N6) (2D) and O(N12) (3D) complexities of averaged models. Crucially, the scale range for augmentation adapts during training to avoid excessive scale invariance. This is the first time nearly exact affine invariance is incorporated into neural networks without directly sampling the entire group. Extensive experiments unequivocally confirm its superiority, achieving new state-of-the-art results in affNIST and SIM2MNIST classifications while consuming less than 15% of inference time and fewer computational resources and model parameters compared to averaged models.

• Klíčová slova
• Affine invariance, Data augmentation, Representation learning, Self-supervised method,
• MeSH
• neuronové sítě * MeSH
• učení * MeSH
• Publikační typ
• časopisecké články MeSH

OBJECTIVE: Supervision is a basic part of training and ongoing education in cognitive behavioural therapy. Self-reflection is an important part of supervision. The conscious understanding of one's own emotions, feelings, thoughts, and attitudes at the time of their occurrence, and the ability to continuously follow and recognize them are among the most important abilities of both therapists and supervisors. The objective of this article is to review aspects related to supervision in cognitive behavioural therapy and self-reflection in the literature. METHODS: This is a narrative review. A literature review was performed using the PubMed, SciVerse Scopus, and Web of Science databases; additional references were found through bibliography reviews of relevant articles published prior to July 2011. The databases were searched for articles containing the following keywords: cognitive behavioural therapy, self-reflection, therapeutic relationship, training, supervision, transference, and countertransference. The review also includes information from monographs referred to by other reviews. RESULTS: We discuss conceptual aspects related to supervision and the role of self-reflection. Self-reflection in therapy is a continuous process which is essential for the establishment of a therapeutic relationship, the professional growth of the therapist, and the ongoing development of therapeutic skills. Recognizing one's own emotions is a basic skill from which other skills necessary for both therapy and emotional self-control stem. Therapists who are skilled in understanding their inner emotions during their encounters with clients are better at making decisions, distinguishing their needs from their clients' needs, understanding transference and countertransference, and considering an optimal response at any time during a session. They know how to handle their feelings so that these correspond with the situation and their response is in the client's best interest. The ability to self-reflect increases the ability to perceive other people's inner emotions, kindles altruism, and increases attunement to subtle signals indicating what others need or want. Self-reflection may be practised by the therapists themselves using traditional cognitive behavioural therapy techniques, or it may be learned in the course of supervision. If therapists are unable to recognize their own thoughts and feelings, or the effects of their attitudes in a therapeutic situation, then they are helpless against these thoughts and feelings, which may control the therapist's behaviour to the disadvantage of the client and therapist alike. CONCLUSION: Training and supervision focused on self-reflection are beneficial to both supervisees and their clients. The more experienced the supervisor is, the more self-reflection used in therapy and supervision.

• MeSH
• kognitivně behaviorální terapie metody   MeSH
• lidé MeSH
• organizace a řízení MeSH
• přenos (psychologie) MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH

The performance of deep learning-based detection methods has made them an attractive option for robotic perception. However, their training typically requires large volumes of data containing all the various situations the robots may potentially encounter during their routine operation. Thus, the workforce required for data collection and annotation is a significant bottleneck when deploying robots in the real world. This applies especially to outdoor deployments, where robots have to face various adverse weather conditions. We present a method that allows an independent car tansporter to train its neural networks for vehicle detection without human supervision or annotation. We provide the robot with a hand-coded algorithm for detecting cars in LiDAR scans in favourable weather conditions and complement this algorithm with a tracking method and a weather simulator. As the robot traverses its environment, it can collect data samples, which can be subsequently processed into training samples for the neural networks. As the tracking method is applied offline, it can exploit the detections made both before the currently processed scan and any subsequent future detections of the current scene, meaning the quality of annotations is in excess of those of the raw detections. Along with the acquisition of the labels, the weather simulator is able to alter the raw sensory data, which are then fed into the neural network together with the labels. We show how this pipeline, being run in an offline fashion, can exploit off-the-shelf weather simulation for the auto-labelling training scheme in a simulator-in-the-loop manner. We show how such a framework produces an effective detector and how the weather simulator-in-the-loop is beneficial for the robustness of the detector. Thus, our automatic data annotation pipeline significantly reduces not only the data annotation but also the data collection effort. This allows the integration of deep learning algorithms into existing robotic systems without the need for tedious data annotation and collection in all possible situations. Moreover, the method provides annotated datasets that can be used to develop other methods. To promote the reproducibility of our research, we provide our datasets, codes and models online.

• Klíčová slova
• inclement weather conditions, long-term autonomy, machine learning, self-supervised learning,
• MeSH
• algoritmy * MeSH
• lidé MeSH
• neuronové sítě * MeSH
• počasí MeSH
• počítačová simulace MeSH
• reprodukovatelnost výsledků MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: Necrosis quantification in the neoadjuvant setting using pathology slide review is the most important validated prognostic marker in conventional osteosarcoma. Herein, we explored three deep-learning strategies on histology samples to predict outcome for osteosarcoma in the neoadjuvant setting. EXPERIMENTAL DESIGN: Our study relies on a training cohort from New York University (NYU; New York, NY) and an external cohort from Charles University (Prague, Czechia). We trained and validated the performance of a supervised approach that integrates neural network predictions of necrosis/tumor content and compared predicted overall survival (OS) using Kaplan-Meier curves. Furthermore, we explored morphology-based supervised and self-supervised approaches to determine whether intrinsic histomorphologic features could serve as a potential marker for OS in the neoadjuvant setting. RESULTS: Excellent correlation between the trained network and pathologists was obtained for the quantification of necrosis content (R2 = 0.899; r = 0.949; P < 0.0001). OS prediction cutoffs were consistent between pathologists and the neural network (22% and 30% of necrosis, respectively). The morphology-based supervised approach predicted OS; P = 0.0028, HR = 2.43 (1.10-5.38). The self-supervised approach corroborated the findings with clusters enriched in necrosis, fibroblastic stroma, and osteoblastic morphology associating with better OS [log-2 hazard ratio (lg2 HR); -2.366; -1.164; -1.175; 95% confidence interval, (-2.996 to -0.514)]. Viable/partially viable tumor and fat necrosis were associated with worse OS [lg2 HR; 1.287; 0.822; 0.828; 95% confidence interval, (0.38-1.974)]. CONCLUSIONS: Neural networks can be used to automatically estimate the necrosis to tumor ratio, a quantitative metric predictive of survival. Furthermore, we identified alternate histomorphologic biomarkers specific to the necrotic and tumor regions, which could serve as predictors.

• MeSH
• deep learning * MeSH
• dospělí MeSH
• Kaplanův-Meierův odhad MeSH
• konvoluční neuronové sítě MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• nádory kostí * mortalita   patologie   terapie   MeSH
• nekróza patologie   MeSH
• neoadjuvantní terapie MeSH
• neuronové sítě * MeSH
• osteosarkom * mortalita   patologie   terapie   MeSH
• prognóza MeSH
• Check Tag
• dospělí MeSH
• lidé MeSH
• mladiství MeSH
• mladý dospělý MeSH
• mužské pohlaví MeSH
• ženské pohlaví MeSH
• Publikační typ
• časopisecké články MeSH

PURPOSE: While the recommended analysis method for magnetic resonance spectroscopy data is linear combination model (LCM) fitting, the supervised deep learning (DL) approach for quantification of MR spectroscopy (MRS) and MR spectroscopic imaging (MRSI) data recently showed encouraging results; however, supervised learning requires ground truth fitted spectra, which is not practical. Moreover, this work investigates the feasibility and efficiency of the LCM-based self-supervised DL method for the analysis of MRS data. METHOD: We present a novel DL-based method for the quantification of relative metabolite concentrations, using quantum-mechanics simulated metabolite responses and neural networks. We trained, validated, and evaluated the proposed networks with simulated and publicly accessible in-vivo human brain MRS data and compared the performance with traditional methods. A novel adaptive macromolecule fitting algorithm is included. We investigated the performance of the proposed methods in a Monte Carlo (MC) study. RESULT: The validation using low-SNR simulated data demonstrated that the proposed methods could perform quantification comparably to other methods. The applicability of the proposed method for the quantification of in-vivo MRS data was demonstrated. Our proposed networks have the potential to reduce computation time significantly. CONCLUSION: The proposed model-constrained deep neural networks trained in a self-supervised manner can offer fast and efficient quantification of MRS and MRSI data. Our proposed method has the potential to facilitate clinical practice by enabling faster processing of large datasets such as high-resolution MRSI datasets, which may have thousands of spectra.

• Klíčová slova
• Convolutional neural network, Deep learning, Inverse problem, MR spectroscopy, Machine learning, Metabolite quantification,
• MeSH
• deep learning * MeSH
• lidé MeSH
• magnetická rezonanční spektroskopie MeSH
• magnetická rezonanční tomografie metody   MeSH
• mozek diagnostické zobrazování   metabolismus   MeSH
• neuronové sítě MeSH
• počítačové zpracování obrazu metody   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Psychotherapy requires clinical supervision. This is systematic guidance of a therapist by a supervisor. Inevitably, there is a question of training new high-quality therapists. This is related to supervision of their basic training. Later, it is important to provide an opportunity for lifelong supervision throughout the entire psychotherapeutic practice. METHOD: PUBMED data base was searched for articles using the key words "supervision in CBT", "therapeutic relations", "transference", "countertransference", "schema therapy", "dialectical behavioral therapy". The search was repeated by changing the key word. No language or time constraints were applied. The lists of references of articles detected by this computer data base search were examined manually to find additional articles. We also used the original texts of A. T. Beck, J. Beck, M. Linehan, R. Leahy, J. Young and others. Basically this is a review with conclusions about supervision in cognitive behavioral therapy. RESULTS: The task of supervision is obvious - to increase the value of the therapeutic process in the client's best interest. At the same time, supervision is an educational process in the truest sense of the word, including an opportunity to select one's own supervisor. This is a very important procedural aspect since the therapist identifies with his/her supervisor, either consciously or unconsciously. Establishing the supervisor-supervisee relationship is based on principles similar to those in the therapeutic relationship. There is an important parallel reflecting the therapist-client relationship. This is because any changes in the supervisory process are analogically transferred onto the therapist-client relationship. Additionally, supervision is oriented towards increasing the therapist's competencies. The CBT therapist's basic skills involve good theoretical knowledge, professional behaviour towards clients, ability to use specific therapeutic strategies for maintaining the therapeutic relationship, sensitivity to parallel processes and accomplishment of changes, and adherence to ethical norms. Given the fact that during supervision, the supervisee may be in any stage of his/ her training, supervision must take into consideration where the therapist is in his/her training and development and what he/she has or has not learnt. CONCLUSIONS: Both the literature and our experience underscore the importance of careful supervision of cognitive behavioral therapy. The supervisory relationship is similar to a therapeutic relationship and the supervisee also needs security, acceptance and appreciation for his/her professional growth. However, there is more freedom in the relationship. Supervision may only lead to the supervisee's professional growth if it supports his/her individuality and helps him/her to discover things. Therefore, numerous approaches are used in supervision which are associated with the abilities to self-reflect and to realize transference and countertransference mechanisms.

• MeSH
• klinické kompetence MeSH
• kognitivně behaviorální terapie výchova   MeSH
• lidé MeSH
• vyučování * MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• práce podpořená grantem MeSH

BACKGROUND: Anorectal dysfunction (ARD), especially bowel incontinence, frequently compromises the quality of life in multiple sclerosis (MS) patients. The effect of rehabilitation procedures has not been clearly established. OBJECTIVE: To determine the effect of an individualized rehabilitation approach on bowel incontinence and anorectal pressures. METHODS: MS patients with ARD underwent 6-months of individually targeted biofeedback rehabilitation. High resolution anorectal manometry (HRAM) and St. Mark's Fecal Incontinence Scores (SMIS) were completed prior to rehabilitation, after 10 weeks of supervised physiotherapy, and after 3 months of self-treatment. RESULTS: Ten patients (50%) completed the study. Repeated measures analysis of variance (ANOVA) demonstrated significant improvement in the SMIS questionnaire over time [14.00 baseline vs. 9.70 after supervised physiotherapy vs. 9.30 after self-treatment (p = 0.005)]. No significant improvements over time were noted in any HRAM readings: maximal pressure [49.85 mmHg baseline vs. 57.60 after supervised physiotherapy vs. 60.88 after self-treatment (p = 0.58)], pressure endurance [36.41 vs. 46.89 vs. 49.95 (p = 0.53)], resting pressure [55.83, vs 52.69 vs. 51.84 (p = 0.704)], or area under the curve [230.0 vs. 520.8 vs. 501.9 (p = 0.16)]. CONCLUSIONS: The proposed individualized rehabilitation program supports a positive overall effect on anorectal dysfunction in MS patients.

• Klíčová slova
• Multiple sclerosis, St. Mark’s fecal incontinence score, biofeedback, bowel incontinence, high resolution anorectal manometry,
• MeSH
• biofeedback (psychologie) MeSH
• fekální inkontinence * etiologie   MeSH
• kvalita života MeSH
• lidé MeSH
• manometrie MeSH
• pilotní projekty MeSH
• roztroušená skleróza * komplikace   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH