nils-herrmann/clean_scirepeval huggingface dataset:
Mass segmentation in mammograms: A cross-sensor comparison of deep and tailored features

Through the years, several CAD systems have been developed to help radiologists in the hard task of detecting signs of cancer in mammograms. In these CAD systems, mass segmentation plays a central role in the decision process. In the literature, mass segmentation has been typically evaluated in a intra-sensor scenario, where the methodology is designed and evaluated in similar data. However, in practice, acquisition systems and PACS from multiple vendors abound and current works fails to take into account the differences in mammogram data in the performance evaluation. In this work it is argued that a comprehensive assessment of the mass segmentation methods requires the design and evaluation in datasets with different properties. To provide a more realistic evaluation, this work proposes: a) improvements to a state of the art method based on tailored features and a graph model; b) a head-to-head comparison of the improved model with recently proposed methodologies based in deep learning and structured prediction on four reference databases, performing a cross-sensor evaluation. The results obtained support the assertion that the evaluation methods from the literature are optimistically biased when evaluated on data gathered from exactly the same sensor and/or acquisition protocol.
Effective Atomic Number of Ge-Doped and Al-Doped Optical Fibers for Radiation Dosimetry Purposes

Optical fibers have been demonstrated by this group to show promising thermoluminescence (TL) properties with respect to ionizing radiation. Present research has focused on commercially produced single-mode telecommunication optical fibers manufactured by CorActive (Canada) either in the form of SiO2 optical fibers doped with either Ge or Al. Control of radiation dose is essential in performing an experiment in a biomedical context. One important aspect in this is the tissue equivalence of the dosimetric material. Mixtures or compounds that are similar in their radiation interaction characteristics to the soft tissue, bone or any other body constituents can be identified for this purpose. Effective atomic number of a medium prescribes its detection efficiency and tissue equivalence. To obtain the effective atomic number of the doped fibers, SEM (Scanning Electron Microscope) and EDXRS (Energy Dispersive X-ray Spectroscopy) analysis was performed to acquire the composition of the element inside the optical fibers. From our investigation, the value of Zeff is in the range of 11.5-13.4 and 11.7-13.7 for Ge-doped and Al-doped respectively (value of Zeff in soft tissue is 7.5 and Zeff for bone is between 11.6-13.8). Given that Ge- and Al-doped optical fibers are not soft-tissue equivalent, the assessment of dose deposition in such media would need to be corrected for an expected over-response. However, the value of Zeff is within the range of bone, making the optical fiber a strong candidate for use in skeletal radiation dosimetry.
Multiple instance learning for breast MRI based on generic spatio-temporal features

In this paper we investigate multiple instance learning (MIL), using generic tile-based spatio-temporal features, for the classification of benign and malignant lesions in breast cancer magnetic resonance imaging (MRI). In particular, we compare the performance of citation-kNN (CkNN) and conventional kNN against a traditional approach based on bespoke features extracted from a segmented region-of-interest (ROI). Results demonstrate that tile-based CkNN has equivalent performance to ROI-based classification. However, the tile-based approach does not require any domain specific features typically used in breast MRI. This not only has the potential to make tile-based classification robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for the detection of suspicious lesions prior to segmentation.
Collapsed-Hole Ge-Doped Photonic Crystal Fiber as a Diagnostic Radiation Dosimeter

A new type of microstructured optical fiber, fabricated by collapsing down the entire array of holes formed by a Photonic Crystal Fiber (referred to herein as PCF-collapsed), is used in this study as an ionizing radiation dosimeter. The performance of the optical fiber is evaluated for diagnostic applications, covering doses from a fraction of 1 mGy up to 10 Gy. The effect of different x-ray beam accelerating potentials from 40 up to 125 kVp photon beam, 6 MeV electrons, and 6 MV photons are demonstrated. The performance of the PCF-collapsed is compared with the uncollapsed PCF (PCF-structured) and a conventional 20-μm-core-diameter optical fiber, all three types of fiber being fabricated from the same Ge-doped-preforms. It is shown that while a PCF-structured has relatively low sensitivity, by collapsing all of the holes in the PCF, the TL response of the fiber is increased by more than 16 times, being also more than four times greater than that of the conventional form optical fiber. The PCF-collapsed can detect a minimum dose as low as 0.6 mGy measured and 5.75 ± 0.11 mGy calculated. The TL analysis suggests that a collapsed-hole-PCF enhances structural defects in an uncollapsed-hole-PCF, highly desirable for dosimeter applications.
Feedback-driven acquisition of artist-generated images with an ultrasonic interface

Though there are a variety of graphics tablets, these do not necessarily allow an artist to interact with the computer as if handling traditional pens and brushes. A study of practising artists indicates that haptic and propriaptic feedback respectively of pen pressure and motion, together with a direct view of the image being created, is required. Flat-screen LCD (Liquid Crystal Display) technology now allows an artist to view an image while the stylus' spatial position is acquired. Through the use of ultrasonic sensors, position can be fixed without electronic interference from the display device, allowing an off-the-shelf LCD to be employed. Provision of other sensors in the stylus, such as pressure, acceleration, rotation, and tilt meters, replicate the experience of the artist when using a conventional drawing/painting method. The design of the interface has been accomplished, at a low cost, with a set of PIC microcontrollers and standard ultrasonic sensors.
Multiple Instance Learning for Breast Cancer Magnetic Resonance Imaging

In this paper we evaluate the suitability of multiple instance learning (MIL) for the classification of T2 weighted magnetic resonance images (MRI) of the breast. Specifically, we compare the performance of citation-kNN against traditional kNN and a random forest (RF) classifier. We utilise both (generic) tile-based features and (domain specific) region-of-interest (ROI) based features We perform experiments on two datasets consisting of A) mass-like lesions and B) both mass-like and non-mass-like lesions. The performance of citation-kNN as both a diagnostic and screening tool is evaluated using the area under the receiver operating characteristics curve (AUC), estimated over 10-fold cross-validation. Results demonstrate that citation- kNN has equivalent performance to traditional kNN and RF. However, the tile-based approach used by citation-kNN does not require the domain specific ROI-based features typically used in breast MRI. This not only makes citation-kNN robust to inaccuracies in the delineation of suspicious lesions, but also makes it suitable for use as a screening tool, where the aim is to discriminate lesions from normal tissue.
Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior

We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does not allow the implementation of a robust DL model that could produce accurate segmentation results on its own. Furthermore, GOCS does not need precise initialisation compared to locally optimal methods on a continuous space (e.g., Mumford-Shah based level set methods); also, GOCS has smaller memory complexity compared to globally optimal inference on a discrete space (e.g., graph cuts). Experimental results show that the proposed method produces the current state-of-the-art mass segmentation (from DCEMRI) results, achieving a mean Dice coefficient of 0.77 for the test set.
Enhancing the classification accuracy of Steady-State Visual Evoked Potential-based Brain-Computer Interface using Component Synchrony Measure

Steady-State Visual Evoked Potential-based (SSVEP) Brain-Computer Interface (BCI) shows great potential as a viable BCI due to its ease of implementation and speed. However, the majority of the SSVEP-BCI implementations use only features from the Power Spectral Density (PSD) despite the fact that upon transforming the signals to the Fourier domain, both the phase and amplitude components are available. In this study we extract the phase response and compute the phase variance as a measure of phase synchrony. This phase synchrony method is called Component Synchrony Measure (CSM). Our results indicate that by including the CSM as a feature, the SSVEP-BCI classification accuracy is significantly enhanced. This further establishes the use of both amplitude and phase information for obtaining good classification accuracy in SSVEP-BCI.
Group-based meta-classification

Virtually all existing classification techniques label one sample at a time. In this paper, we highlight the potential benefits of group based classification (GBC), where the classifier labels a group of homogeneous samples. In this way, GBC can take advantage of the additional prior knowledge that all samples belong to the same, unknown, class. We pose GBC in a generic hypothesis testing framework requiring the selection of an appropriate sample and test statistic. We then evaluate one simple example of GBC on both synthetic and real data sets and demonstrate that GBC may be a promising approach in applications where the test data can be arranged into homogenous subsets.
On the methodology for comparing learning algorithms: a case

We explore several issues relevant to the benchmarking and comparison of machine learning algorithms. We illustrate those issues with a case study using the decision tree induction algorithms C4.5 ( 1993) and multiscale classification (MSC) (A.P. Bradley and B.C. Lovell, 1994), multilayer perceptrons (MLP) and multivariable regression (MVR). Then for a "real world" problem we compare estimates of the true error rates for each classifier, first on a single train and test partition, and then using cross validated subsampling techniques. The relevance of the /spl chi//sup 2/ test is then discussed in relation to comparing the classifier accuracies. The paper concludes by evaluating the performance of these four fundamentally different approaches to the solution of this regression problem.<<ETX>>
A System to Generate Patient-Specific Stimuli for use with the Auditory Brainstem Response Test

The short-pulsed "click" stimuli most commonly used to evoke an auditory brainstem response (ABR) do not account for temporal shifts along the basilar membrane and do not produce ABRs of maximal amplitude. This paper describes a system that maps these temporal shifts and generates patient-specific stimuli to compensate. This is of interest both to enhance the ABR and as a potential way to map the physiology of the basilar membrane.
Performance Analysis of Three Microscope Slide Scanning Techniques

The demands for digital pathology systems have increased dramatically in the last decade as Virtual Microscopy (VM) has gained increasing popularity. Many digital slide acquisition systems have been developed to meet this demand, utilising a variety of image scan techniques. However, the requirements for, and performance of, these scan techniques are largely undocumented. Therefore, in this paper we evaluate the three primary approaches to digital slide scanning in light field microscopy: field-of-view (FOV) scan, line scan and slanted specimen scan. Initially, we develop equations for each technique that estimates their theoretical scan times in terms data throughput rates. Next, we compare each system's performance based on the relationships between illumination, camera frame rates, data transfer rates and microscope stage speed. We conclude that slanted scan system capable of acquiring multiple focal planes in one pass have the potential to obtain the shortest scan times within current constraints on stage and camera hardware.
The Multiscale Classifier

In this paper we propose a rule-based inductive learning algorithm called Multisca/e Classification (MSC). It can be applied to any N-dimensional real or binary classification problem to classify the training data by successively splitting the feature space in half. The algorithm has several significant differences from existing rule-based approaches: learning is incremental, the tree is non-binary, and backtracking of decisions is possible to some extent. The paper first provides background on current machine learning techniques and outlines some of their strengths and weaknesses. It then describes the MSC algorithm and compares it to other inductive learning algorithms with particular reference to IDS, C4.5, and back-propagation neural networks. Its performance on a number of standard benchmark problems is then discussed and related to standard learning issues such as generalization, representational power, and over-specialization.
Anatomical Skin Segmentation in Reflectance Confocal Microscopy with Weak Labels

Reflectance confocal microscopy (RCM) allows in-vivo microscopic examination of human skin and is emerging as a powerful tool for a wide range of dermatological problems. Clinical use of RCM is limited by the need for trained experts to interpret images and the lack of supporting tools for quantitative evaluation of the images, especially in large datasets. The first task in understanding RCM images is to understand and produce a segmentation of the anatomical strata of human skin. This work presents such an algorithm using only weak supervision, in the form of labels for whole en-face sections, to learn a per pixel segmentation of a complete RCM depth stack. Using a bag-of- features representation for image appearance, and a conditional random field model for strata labels through the depth of the skin, a structured support vector machine was trained to label individual pixels. The approach was developed and tested on a dataset of 308 depth stacks from 54 volunteers, consisting of 16,144 total en-face sections. This approach accurately classified 85.7% of sections in the test set, and was able to detect underlying changes in the skin strata thickness with age.
On the Estimation of Extrinsic and Intrinsic Parameters of Optical Microscope Calibration

This paper compares several camera calibration methods on the estimation of specific extrinsic and intrinsic parameters. Good estimates of the chosen parameters, rotation and radial lens distortion are essential to increase the accuracy of quantitative measurements and to accurately stitch single field-of-view-based images together. The parameters are obtained using two selected methods on different objective magnifications on a microscope system using a fixed grid calibration pattern. We evaluate two methods and show that the rotation angles from one of the methods is consistent with a simple homography while the other estimates a consistently smaller angle. The radial distortion estimates are both very small and relate to a distortion of less than one pixel.
Automated 5-year mortality prediction using deep learning and radiomics features from chest computed tomography

In this paper, we propose new prognostic methods that predict 5-year mortality in elderly individuals using chest computed tomography (CT). The methods consist of a classifier that performs this prediction using a set of features extracted from the CT image and segmentation maps of multiple anatomic structures. We explore two approaches: 1) a unified framework based on two state-of-the-art deep learning models extended to 3-D inputs, where features and classifier are automatically learned in a single optimisation process; and 2) a multi-stage framework based on the design and selection and extraction of hand-crafted radiomics features, followed by the classifier learning process. Experimental results, based on a dataset of 48 annotated chest CTs, show that the deep learning models produces a mean 5-year mortality prediction AUC in [68.8%,69.8%] and accuracy in [64.5%,66.5%], while radiomics produces a mean AUC of 64.6% and accuracy of 64.6%. The successful development of the proposed models has the potential to make a profound impact in preventive and personalised healthcare.
Sample size estimation using the receiver operating characteristic curve

In This work we describe two related approaches to estimating the sample sizes required to statistically compare the performance of two classifiers: acceptable failure rates (AFR) and the area under the receiver operating characteristic (ROC) curve (AUC). In particular, we consider rare event detection problems, where the prior class probabilities are highly skewed, and measure performance at a specific operating point and for the whole ROC curve. It is shown that the use of AUC as a performance measure is preferable to AFR as it requires a smaller data set to demonstrate superiority of one classifier over another.
Evaluation of Three Algorithms for the Segmentation of Overlapping Cervical Cells

In this paper, we introduce and evaluate the systems submitted to the first Overlapping Cervical Cytology Image Segmentation Challenge, held in conjunction with the IEEE International Symposium on Biomedical Imaging 2014. This challenge was organized to encourage the development and benchmarking of techniques capable of segmenting individual cells from overlapping cellular clumps in cervical cytology images, which is a prerequisite for the development of the next generation of computer-aided diagnosis systems for cervical cancer. In particular, these automated systems must detect and accurately segment both the nucleus and cytoplasm of each cell, even when they are clumped together and, hence, partially occluded. However, this is an unsolved problem due to the poor contrast of cytoplasm boundaries, the large variation in size and shape of cells, and the presence of debris and the large degree of cellular overlap. The challenge initially utilized a database of <inline-formula><tex-math notation="LaTeX">$16$</tex-math></inline-formula> high-resolution (<inline-formula><tex-math notation="LaTeX">$\times$</tex-math></inline-formula>40 magnification) images of complex cellular fields of view, in which the isolated real cells were used to construct a database of <inline-formula><tex-math notation="LaTeX">$945$</tex-math></inline-formula> cervical cytology images synthesized with a varying number of cells and degree of overlap, in order to provide full access of the segmentation ground truth. These synthetic images were used to provide a reliable and comprehensive framework for quantitative evaluation on this segmentation problem.  This highlights the intrinsic difficulty of this challenge and provides motivation for significant future improvement.
Efficient micro-electrode recording modeling using a filtered point process

In this paper we present an eﬃcient model of the neuronal potentials recorded by a deep brain stimulation microelectrode (DBS MER) in the subthalamic nucleus. It is shown that a computationally eﬃcient ﬁltered point process consisting of 10,000 neurons, including extracellular ﬁltering closely matches recordings from 14 Parkinson’s disease patients. The recordings were compared using their voltage amplitude distributions, power spectral density estimates and phase synchrony. It was found that interspike interval times modeled using a Weibull distribution with a shape parameter of 0.8, slightly non-Poisosnian, gave the best ﬁt of the simulations to patient recordings. These results indicate that part of the ‘background activity’ present in an DBS MER can be considered to be a very local ﬁeld potential due to the surrounding neuronal activity.Therefore, the statistics of the interspike interval times modify the structure of the background activity.
Automated Mass Detection in Mammograms Using Cascaded Deep Learning and Random Forests

Mass detection from mammograms plays a crucial role as a pre- processing stage for mass segmentation and classification. The detection of masses from mammograms is considered to be a challenging problem due to their large variation in shape, size, boundary and texture and also because of their low signal to noise ratio compared to the surrounding breast tissue. In this paper, we present a novel approach for detecting masses in mammograms using a cascade of deep learning and random forest classifiers. The first stage classifier consists of a multi-scale deep belief network that selects suspicious regions to be further processed by a two-level cascade of deep convolutional neural networks. The regions that survive this deep learning analysis are then processed by a two-level cascade of random forest classifiers that use morphological and texture features extracted from regions selected along the cascade. Finally, regions that survive the cascade of random forest classifiers are combined using connected component analysis to produce state-of-the-art results. We also show that the proposed cascade of deep learning and random forest classifiers are effective in the reduction of false positive regions, while maintaining a high true positive detection rate. We tested our mass detection system on two publicly available datasets: DDSM-BCRP and INbreast. The final mass detection produced by our approach achieves the best results on these publicly available datasets with a true positive rate of 0.96 ± 0.03 at 1.2 false positive per image on INbreast and true positive rate of 0.75 at 4.8 false positive per image on DDSM-BCRP.
Automated analysis of the auditory brainstem response

We describe an algorithm for automated peak-trough labelling of the auditory brainstem response (ABR). The algorithm finds the peaks of clinical interest by estimating the first and second derivatives of the ABR waveform. Prior knowledge regarding the expected latencies and amplitudes of the various peaks is then used to label the peaks incrementally I to VII. The performance of the algorithm is estimated on a set of 240 ABR waveforms captured at a stimulus intensity of 90 dBnHL. The proposed algorithm not only offers insight into the manual process of ABR peak detection, but is shown to be extremely, accurate (96-98%) at finding the primary peaks of clinical interest (I, III, and V). It is also shown that although peak IV can only be found with moderate accuracy (77%), almost one third of these correct detections come as a direct result of locating the "peak" using the second derivative.