João F. Teixeira

Bio: João F. Teixeira is an academic researcher from University of Porto. The author has contributed to research in topics: Segmentation & Breast cancer. The author has an hindex of 4, co-authored 18 publications receiving 35 citations.

A Regression Model for Predicting Shape Deformation after Breast Conserving Surgery.

Abstract: Breast cancer treatments can have a negative impact on breast aesthetics, in case when surgery is intended to intersect tumor. For many years mastectomy was the only surgical option, but more recently breast conserving surgery (BCS) has been promoted as a liable alternative to treat cancer while preserving most part of the breast. However, there is still a significant number of BCS intervened patients who are unpleasant with the result of the treatment, which leads to self-image issues and emotional overloads. Surgeons recognize the value of a tool to predict the breast shape after BCS to facilitate surgeon/patient communication and allow more educated decisions; however, no such tool is available that is suited for clinical usage. These tools could serve as a way of visually sensing the aesthetic consequences of the treatment. In this research, it is intended to propose a methodology for predict the deformation after BCS by using machine learning techniques. Nonetheless, there is no appropriate dataset containing breast data before and after surgery in order to train a learning model. Therefore, an in-house semi-synthetic dataset is proposed to fulfill the requirement of this research. Using the proposed dataset, several learning methodologies were investigated, and promising outcomes are obtained.

Lightweight Deep Learning Pipeline for Detection, Segmentation and Classification of Breast Cancer Anomalies.

Abstract: The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods.

InspirerMundi-Remote Monitoring of Inhaled Medication Adherence through Objective Verification Based on Combined Image Processing Techniques.

Abstract: Background The adherence to inhaled controller medications is of critical importance for achieving good clinical results in patients with chronic respiratory diseases. Self-management strategies can result in improved health outcomes and reduce unscheduled care and improve disease control. However, adherence assessment suffers from difficulties on attaining a high grade of trustworthiness given that patient self-reports of high-adherence rates are known to be unreliable. Objective Aiming to increase patient adherence to medication and allow for remote monitoring by health professionals, a mobile gamified application was developed where a therapeutic plan provides insight for creating a patient-oriented self-management system. To allow a reliable adherence measurement, the application includes a novel approach for objective verification of inhaler usage based on real-time video capture of the inhaler's dosage counters. Methods This approach uses template matching image processing techniques, an off-the-shelf machine learning framework, and was developed to be reusable within other applications. The proposed approach was validated by 24 participants with a set of 12 inhalers models. Results Performed tests resulted in the correct value identification for the dosage counter in 79% of the registration events with all inhalers and over 90% for the three most widely used inhalers in Portugal. These results show the potential of exploring mobile-embedded capabilities for acquiring additional evidence regarding inhaler adherence. Conclusion This system helps to bridge the gap between the patient and the health professional. By empowering the first with a tool for disease self-management and medication adherence and providing the later with additional relevant data, it paves the way to a better-informed disease management decision.

Three-Dimensional Planning Tool for Breast Conserving Surgery: A Technological Review

Abstract: Breast cancer is one of the most common malignancies affecting women worldwide. However, despite its incidence trends have increased, the mortality rate has significantly decreased. The primary concern in any cancer treatment is the oncological outcome but, in the case of breast cancer, the surgery aesthetic result has become an important quality indicator for breast cancer patients. In this sense, an adequate surgical planning and prediction tool would empower the patient regarding the treatment decision process, enabling a better communication between the surgeon and the patient and a better understanding of the impact of each surgical option. To develop such tool, it is necessary to create complete 3D model of the breast, integrating both inner and outer breast data. In this review, we thoroughly explore and review the major existing works that address, directly or not, the technical challenges involved in the development of a 3D software planning tool in the field of breast conserving surgery.

Biomechanics And Motor Control Of Human Movement

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Patient-physician discordance in assessment of adherence to inhaled controller medication: a cross-sectional analysis of two cohorts

Abstract: Objective We aimed to compare patient’s and physician’s ratings of inhaled medication adherence and to identify predictors of patient-physician discordance. Design Baseline data from two prospective multicentre observational studies. Setting 29 allergy, pulmonology and paediatric secondary care outpatient clinics in Portugal. Participants 395 patients (≥13 years old) with persistent asthma. Measures Data on demographics, patient-physician relationship, upper airway control, asthma control, asthma treatment, forced expiratory volume in one second (FEV1) and healthcare use were collected. Patients and physicians independently assessed adherence to inhaled controller medication during the previous week using a 100 mm Visual Analogue Scale (VAS). Discordance was defined as classification in distinct VAS categories (low 0–50; medium 51–80; high 81–100) or as an absolute difference in VAS scores ≥10 mm. Correlation between patients’ and physicians’ VAS scores/categories was explored. A multinomial logistic regression identified the predictors of physician overestimation and underestimation. Results High inhaler adherence was reported both by patients (median (percentile 25 to percentile 75) 85 (65–95) mm; 53% VAS>80) and by physicians (84 (68–95) mm; 53% VAS>80). Correlation between patient and physician VAS scores was moderate (rs=0.580; p Conclusion Although both patients and physicians report high inhaler adherence, discordance occurred in half of cases. Implementation of objective adherence measures and effective communication are needed to improve patient-physician agreement.

Digital Inhalers for Asthma or Chronic Obstructive Pulmonary Disease: A Scientific Perspective

Abstract: Impressive advances in inhalation therapy for patients with asthma and chronic obstructive pulmonary disease (COPD) have occurred in recent years. However, important gaps in care remain, particularly relating to poor adherence to inhaled therapies. Digital inhaler health platforms which incorporate digital inhalers to monitor time and date of dosing are an effective disease and medication management tool, promoting collaborative care between clinicians and patients, and providing more in-depth understanding of actual inhaler use. With advances in technology, nearly all inhalers can be digitalized with add-on or embedded sensors to record and transmit data quantitating inhaler actuations, and some have additional capabilities to evaluate inhaler technique. In addition to providing an objective and readily available measure of adherence, they allow patients to interact with the device directly or through their self-management smartphone application such as via alerts and recording of health status. Clinicians can access these data remotely and during patient encounters, to better inform them about disease status and medication adherence and inhaler technique. The ability for remote patient monitoring is accelerating interest in and the use of these devices in clinical practice and research settings. More than 20 clinical studies of digital inhalers in asthma or COPD collectively show improvement in medication adherence, exacerbation risk, and patient outcomes with digital inhalers. These studies support previous findings about patient inhaler use and behaviors, but with greater granularity, and reveal some new findings about patient medication-taking behaviors. Digital devices that record inspiratory flows with inhaler use can guide proper inhaler technique and may prove to be a clinically useful lung function measure. Adoption of digital inhalers into practice is still early, and additional research is needed to determine patient and clinician acceptability, the appropriate place of these devices in the therapeutic regimen, and their cost effectiveness.

Fully Automatic Computer-aided Mass Detection and Segmentation via Pseudo-color Mammograms and Mask R-CNN

Abstract: Mammographic mass detection and segmentation are usually performed as serial and separate tasks, with segmentation often only performed on manually confirmed true positive detections in previous studies. We propose a fully-integrated computer-aided detection (CAD) system for simultaneous mammographic mass detection and segmentation without user intervention. The proposed CAD only consists of a pseudo-color image generation and a mass detection-segmentation stage based on Mask R-CNN. Grayscale mammograms are transformed into pseudo-color images based on multi-scale morphological sifting where mass-like patterns are enhanced to improve the performance of Mask R-CNN. Transfer learning with the Mask R-CNN is then adopted to simultaneously detect and segment masses on the pseudo-color images. Evaluated on the public dataset INbreast, the method outperforms the state-of-the-art methods by achieving an average true positive rate of 0.90 at 0.9 false positive per image and an average Dice similarity index of 0.88 for mass segmentation.

Transfer Learning and Fine Tuning in Mammogram BI-RADS Classification

Abstract: The BI-RADS report system is widely used by radiologists and clinicians to document relevant findings in the mammogram exam by using a 6 category final assessment. Deep learning has achieved a high level of accuracy in multi category classification of natural images. Because of that, it is of interest to address the mammography malignancy classification according to the established BI-RADS categories. In this work, we use transfer learning on NASNet Mobile and fine tuning on VGG16 and VGG19 to classify mammogram images according to the BI-RADS scale on the INbreast dataset. Our proposed methodology achieved an accuracy (ACC) of 90.9% and a macro averaged area under the receiver operating characteristic curve (AUC) of 99.0%; outperforming some of the similar works found in the literature review.