Modification of the mean-square error principle to double the convergence speed of a special case of Hopfield neural network used to segment pathological liver color images

BACKGROUND: This paper analyzes the effect of the mean-square error principle on the optimization process using a Special Case of Hopfield Neural Network (SCHNN). METHODS: The segmentation of multidimensional medical and colour images can be formulated as an energy function composed of two terms: the sum of squared errors, and a noise term used to avoid the network to be stacked in early local minimum points of the energy landscape. RESULTS: Here, we show that the sum of weighted error, higher than simple squared error, leads the SCHNN classifier to reach faster a local minimum closer to the global minimum with the assurance of acceptable segmentation results. CONCLUSIONS: The proposed segmentation method is used to segment 20 pathological liver colour images, and is shown to be efficient and very effective to be implemented for use in clinics

Use of mobile device technology to continuously collect patient-reported symptoms during radiation therapy for head and neck cancer: A prospective feasibility study

Purpose: Accurate assessment of toxicity allows for timely delivery of supportive measures during radiation therapy for head and neck cancer. The current paradigm requires weekly evaluation of patients by a provider. The purpose of this study is to evaluate the feasibility of monitoring patient reported symptoms via mobile devices. Methods and materials: We developed a mobile application for patients to report symptoms in 5 domains using validated questions. Patients were asked to report symptoms using a mobile device once daily during treatment or more often as needed. Clinicians reviewed patient-reported symptoms during weekly symptom management visits and patients completed surveys regarding perceptions of the utility of the mobile application. The primary outcome measure was patient compliance with mobile device reporting. Compliance is defined as number of days with a symptom report divided by number of days on study. Results: There were 921 symptom reports collected from 22 patients during treatment. Median reporting compliance was 71% (interquartile range, 45%-80%). Median number of reports submitted per patient was 34 (interquartile range, 21-53). Median number of reports submitted by patients per week was similar throughout radiation therapy and there was significant reporting during nonclinic hours. Patients reported high satisfaction with the use of mobile devices to report symptoms. Conclusions: A substantial percentage of patients used mobile devices to continuously report symptoms throughout a course of radiation therapy for head and neck cancer. Future studies should evaluate the impact of mobile device symptom reporting on improving patient outcomes

Ensemble Modeling of the Likely Public Health Impact of a Pre-Erythrocytic Malaria Vaccine

Using an ensemble modeling approach, Thomas Smith and colleagues find that targeted mass vaccination with a pre-erythrocytic malaria vaccine RTS,S in low-transmission settings might have better health effects than vaccination through national EPI programs

Deformable M-Reps for 3D Medical Image Segmentation

M-reps (formerly called DSLs) are a multiscale medial means for modeling and rendering 3D solid geometry. They are particularly well suited to model anatomic objects and in particular to capture prior geometric information effectively in deformable models segmentation approaches. The representation is based on figural models, which define objects at coarse scale by a hierarchy of figures – each figure generally a slab representing a solid region and its boundary simultaneously. This paper focuses on the use of single figure models to segment objects of relatively simple structure