Image forensics has attracted wide attention during the past decade. However, most existing works aim at detecting a certain operation, which means that their proposed features usually depend on the investigated image operation and they consider only binary classification. This usually leads to misleading results if irrelevant features and/or classifiers are used. For instance, a JPEG decompressed image would be classified as an original or median filtered image if it was fed into a median filtering detector. Hence, it is important to develop forensic methods and universal features that can simultaneously identify multiple image operations. Based on extensive experiments and analysis, we find that any image operation, including existing anti-forensics operations, will inevitably modify a large number of pixel values in the original images. Thus, some common inherent statistics such as the correlations among adjacent pixels cannot be preserved well. To detect such modifications, we try to analyze the properties of local pixels within the image in the residual domain rather than the spatial domain considering the complexity of the image contents. Inspired by image steganalytic methods, we propose a very compact universal feature set and then design a multiclass classification scheme for identifying many common image operations. In our experiments, we tested the proposed features as well as several existing features on 11 typical image processing operations and four kinds of anti-forensic methods. The experimental results show that the proposed strategy significantly outperforms the existing forensic methods in terms of both effectiveness and universality.

Identification of Various Image Operations Using Residual-Based Features

The current simulation technology used for neurosurgical training leaves much to be desired. Significant efforts are thoroughly exhausted in hopes of developing simulations that translate to give learners the "real-life" feel. Though a respectable goal, this may not be necessary as the application for simulation in neurosurgical training may be most useful in early learners. The ultimate uniformly agreeable endpoint of improved outcome and patient safety drives these investments. We explore the development, availability, educational taskforces, cost burdens and the simulation advancements in neurosurgical training. The technologies can be directed at achieving early resident milestones placed by the Accreditation Council for Graduate Medical Education. We discuss various aspects of neurosurgery disciplines with specific technologic advances of simulation software. An overview of the scholarly landscape of the recent publications in the realm of medical simulation and virtual reality pertaining to neurologic surgery is provided. We analyze concurrent concept overlap between PubMed headings and provide a graphical overview of the associations between these terms.

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Simulation training in neurosurgery: advances in education and practice

The purpose of creating the virtual reality (VR) simulator is to facilitate and supplement the training opportunities provided to orthopedic residents. The use of VR simulators has increased rapidly in the field of medical surgery for training purposes. This paper discusses the creation of the virtual surgical environment (VSE) for training residents in an orthopedic surgical process called less invasive stabilization system (LISS) surgery which is used to address fractures of the femur. The overall methodology included first obtaining an understanding of the LISS plating process through interactions with expert orthopedic surgeons and developing the information centric models. The information centric models provided a structured basis to design and build the simulator. Subsequently, the haptic-based simulator was built. Finally, the learning assessments were conducted in a medical school. The results from the learning assessments confirm the effectiveness of the VSE for teaching medical residents and students. The scope of the assessment was to ensure (1) the correctness and (2) the usefulness of the VSE. Out of 37 residents/students who participated in the test, 32 showed improvements in their understanding of the LISS plating surgical process. A majority of participants were satisfied with the use of teaching Avatars and haptic technology. A paired t test was conducted to test the statistical significance of the assessment data which showed that the data were statistically significant. This paper demonstrates the usefulness of adopting information centric modeling approach in the design and development of the simulator. The assessment results underscore the potential of using VR-based simulators in medical education especially in orthopedic surgery.

An advanced simulator for orthopedic surgical training.

https://ubibliorum.ubi.pt/bitstream/10400.6/9176/1/ResultsMICHE2.pdf

Results from MICHE II Mobile Iris CHallenge Evaluation II

The ability to exert an appropriate amount of force on brain tissue during surgery is an important component of instrument handling. It allows surgeons to achieve the surgical objective effectively while maintaining a safe level of force in tool–tissue interaction. At the present time, this knowledge, and hence skill, is acquired through experience and is qualitatively conveyed from an expert surgeon to trainees. These forces can be assessed quantitatively by retrofitting surgical tools with sensors, thus providing a mechanism for improved performance and safety of surgery, and enhanced surgical training. This paper presents the development of a force-sensing bipolar forceps, with installation of a sensory system, that is able to measure and record interaction forces between the forceps tips and brain tissue in real time. This research is an extension of a previous research where a bipolar forceps was instrumented to measure dissection and coagulation forces applied in a single direction. Here, a planar forceps with two sets of strain gauges in two orthogonal directions was developed to enable measuring the forces with a higher accuracy. Implementation of two strain gauges allowed compensation of strain values due to deformations of the forceps in other directions (axial stiffening) and provided more accurate forces during microsurgery. An experienced neurosurgeon performed five neurosurgical tasks using the axial setup and repeated the same tasks using the planar device. The experiments were performed on cadaveric brains. Both setups were shown to be capable of measuring real-time interaction forces. Comparing the two setups, under the same experimental condition, indicated that the peak and mean forces quantified by planar forceps were at least 7% and 10% less than those of axial tool, respectively; therefore, utilizing readings of all strain gauges in planar forceps provides more accurate values of both peak and mean forces than axial forceps. Cross-correlation analysis between the two force signals obtained, one from each cadaveric practice, showed a high similarity between the two force signals.

A Force-Sensing Bipolar Forceps to Quantify Tool–Tissue Interaction Forces in Microsurgery

A proposed real-time neurosurgery simulator handles skull drilling and surgical interaction with the brain. This involves the development and combination of areas such as collision handling, haptic rendering, physical simulation, and volumetric visualization. The simulator's input data comes from computed-tomography and magnetic-resonance-imaging images of the patients. Collision detection for drilling uses only density data; collision detection for interaction with the brain is based on uniform spatial subdivision of a tetrahedral mesh. To take advantage of all the information, the simulator employs visualization methods such as volumetric isosurfaces and deformable volume rendering.

A Brain Surgery Simulator

Iris matching by means of Machine Learning paradigms

Undirected cyclic graph based multiclass pair-wise classifier

Regression methods aim to predict a numerical value of a target variable given some input variables by building a function $$f:\mathbb {R}^n \rightarrow \mathbb {R}$$ . In Industry 4.0 regression tasks, tabular data-sets are especially frequent. Decision Trees, ensemble methods such as Gradient Boosting and Random Forest, or Support Vector Machines are widely used for regression tasks with tabular data. However, Deep Learning approaches are rarely used with this type of data, due to, among others, the lack of spatial correlation between features. Therefore, in this research, we propose two Deep Learning approaches for working with tabular data. Specifically, two Convolutional Neural Networks architectures are tested against different state of the art regression methods. We perform an hyper-parameter tuning of all the techniques and compare the model performance in different industrial tabular data-sets. Experimental results show that both Convolutional Neural Network approaches can outperform the commonly used methods for regression tasks. 

Goretti Echegaray

Papers

A Brain Surgery Simulator

Iris matching by means of Machine Learning paradigms

Undirected cyclic graph based multiclass pair-wise classifier

Convolutional Neural Networks for Structured Industrial Data