nils-herrmann/clean_scirepeval huggingface dataset:
An Improved Joint Optimization of Multiple Level Set Functions for the Segmentation of Overlapping Cervical Cells

In this paper, we present an improved algorithm for the segmentation of cytoplasm and nuclei from clumps of overlapping cervical cells. This problem is notoriously difficult because of the degree of overlap among cells, the poor contrast of cell cytoplasm and the presence of mucus, blood, and inflammatory cells. Our methodology addresses these issues by utilizing a joint optimization of multiple level set functions, where each function represents a cell within a clump, that have both unary (intracell) and pairwise (intercell) constraints. The unary constraints are based on contour length, edge strength, and cell shape, while the pairwise constraint is computed based on the area of the overlapping regions. In this way, our methodology enables the analysis of nuclei and cytoplasm from both free-lying and overlapping cells. We provide a systematic evaluation of our methodology using a database of over 900 images generated by synthetically overlapping images of free-lying cervical cells, where the number of cells within a clump is varied from 2 to 10 and the overlap coefficient between pairs of cells from 0.1 to 0.5. This quantitative assessment demonstrates that our methodology can successfully segment clumps of up to 10 cells, provided the overlap between pairs of cells is <;0.2. Moreover, if the clump consists of three or fewer cells, then our methodology can successfully segment individual cells even when the overlap is ~0.5. We also evaluate our approach quantitatively and qualitatively on a set of 16 extended depth of field images, where we are able to segment a total of 645 cells, of which only ~10% are free-lying. Finally, we demonstrate that our method of cell nuclei segmentation is competitive when compared with the current state of the art.
Virtual Microscopy with Extended Depth of Field

In this paper, we describe a virtual microscope system, based on JPEG 2000, which utilizes extended depth of field (EDF) imaging. Through a series of observer trials we show that EDF imaging improves both the local image quality of individual fields of view (FOV) and the accuracy with which the FOVs can be mosaiced (stitched) together. In addition, we estimate the required bit rate to adequately render a set of histology and cytology specimens at a quality suitable for on-line learning and collaboration. We show that, using JPEG 2000, we can efficiently represent high-quality, high-resolution colour images of microscopic specimens with less than 1 bit per pixel.
Deep structured learning for mass segmentation from mammograms

In this paper, we present a novel method for the segmentation of breast masses from mammograms exploring structured and deep learning. Specifically, using structured support vector machine (SSVM), we formulate a model that combines different types of potential functions, including one that classifies image regions using deep learning. Our main goal with this work is to show the accuracy and efficiency improvements that these relatively new techniques can provide for the segmentation of breast masses from mammograms. We also propose an easily reproducible quantitative analysis to assess the performance of breast mass segmentation methodologies based on widely accepted accuracy and running time measurements on public datasets, which will facilitate further comparisons for this segmentation problem. In particular, we use two publicly available datasets (DDSM-BCRP and INbreast) and propose the computation of the running time taken for the methodology to produce a mass segmentation given an input image and the use of the Dice index to quantitatively measure the segmentation accuracy. For both databases, we show that our proposed methodology produces competitive results in terms of accuracy and running time.
A new denoising method for dynamic contrast-enhanced MRI

This paper presents a new algorithm for denoising dynamic contrast-enhanced (DCE) MR images. The algorithm is called Dynamic Non-Local Means and is a novel variation on the Non-Local Means (NL-Means) algorithm. It exploits the redundancy of information in the DCE-MRI sequence of images. An evaluation of the performance of the algorithm relative to six other denoising algorithms—Gaussian filtering, the original NL-Means algorithm, bilateral filtering, anisotropic diffusion filtering, the wavelets adaptive multiscale products threshold method, and the traditional wavelet thresholding method—is also presented. The evaluation was performed by two groups of expert observers—18 signal/image processing experts, and 9 clinicians (8 radiographers and 1 radiologist)—using real DCE-MRI data. The results of the evaluation provide evidence, at the α=0.05 level of significance, that both groups of observers deem the DNLM algorithm to perform visually better than all of the other algorithms.
A Comparison of Multiple Instance and Group Based Learning

In this paper we compare the performance of a number of multiple-instance learning (MIL) and group based (GB) classification algorithms on both a synthetic and real-world Pap smear dataset. We utilise the synthetic dataset to demonstrate that performance improves as both bag size and percent positives increase and that MIL outperforms GB algorithms when the percentage positives is less than 50%. However, as the positive bags become increasingly homogeneous, as is apparent on the real-world dataset, the two approaches become comparable. This result highlights that the performance of a MIL or GB algorithm will be maximised when the algorithm's MIL assumption matches the reality of the dataset. Therefore, on the Pap smear dataset, algorithms with a more generalised MIL assumption demonstrate the strongest performance.
Precision-recall operating characteristic (P-ROC) curves in imprecise environments

Traditionally, machine learning algorithms have been evaluated in applications where assumptions can be reliably made about class priors and/or misclassification costs. In this paper, we consider the case of imprecise environments, where little may be known about these factors and they may well vary significantly when the system is applied. Specifically, the use of precision-recall analysis is investigated and compared to the more well known performance measures such as error-rate and the receiver operating characteristic (ROC). We argue that while ROC analysis is invariant to variations in class priors, this invariance in fact hides an important factor of the evaluation in imprecise environments. Therefore, we develop a generalised precision-recall analysis methodology in which variation due to prior class probabilities is incorporated into a multi-way analysis of variance (ANOVA). The increased sensitivity and reliability of this approach is demonstrated in a remote sensing application
Characterization of movements during restless sleep in children: A pilot study

Actigraphy is effective at monitoring circadian rhythms, but often misidentifies periods of restless sleep (defined here as sleep periods with movement) as wake, and periods of quiet wake as sleep. This limitation restricts the effectiveness of actigraphy for investigating sleep disorders. Our objective in this study was to investigate a time-frequency representation of movement during sleep and wake which could ultimately aid in improving classification performance by reducing false wake detections. As a pilot study, we investigate the characteristics of manually labelled movements from six patients (aged 6-12 years, 3 male) during sleep and wake using the over complete discrete wavelet decomposition. The difference between the median wavelet coefficients were analyzed for 30 movement segments from six movement categories during sleep and wake. We found that, in general, the temporal location of high energy coefficients and the energy of the high frequency bands differed between movements during sleep and wake. This indicates that we are able to differentiate movement during sleep and wake with a time-frequency representation. This representation may improve the sleep and wake classification performance by identifying movements specific to sleep and wake. This will likely improve the poor specificity inherent in conventional actigraphy.
Automated Analysis of Unregistered Multi-View Mammograms With Deep Learning

We describe an automated methodology for the analysis of unregistered cranio-caudal (CC) and medio-lateral oblique (MLO) mammography views in order to estimate the patient’s risk of developing breast cancer. The main innovation behind this methodology lies in the use of deep learning models for the problem of jointly classifying unregistered mammogram views and respective segmentation maps of breast lesions (i.e., masses and micro-calcifications). This is a holistic methodology that can classify a whole mammographic exam, containing the CC and MLO views and the segmentation maps, as opposed to the classification of individual lesions, which is the dominant approach in the field. We also demonstrate that the proposed system is capable of using the segmentation maps generated by automated mass and micro-calcification detection systems, and still producing accurate results. The semi-automated approach (using manually defined mass and micro-calcification segmentation maps) is tested on two publicly available data sets (INbreast and DDSM), and results show that the volume under ROC surface (VUS) for a 3-class problem (normal tissue, benign, and malignant) is over 0.9, the area under ROC curve (AUC) for the 2-class “benign versus malignant” problem is over 0.9, and for the 2-class breast screening problem (malignancy versus normal/benign) is also over 0.9. For the fully automated approach, the VUS results on INbreast is over 0.7, and the AUC for the 2-class “benign versus malignant” problem is over 0.78, and the AUC for the 2-class breast screening is 0.86.
An Evaluation of Multi-resolution Microscope Slide Scanning Algorithms

In this paper we develop and compare several multi-resolution microscope slide scanning algorithms of increasing complexity. The algorithms utilise a low-resolution image of the whole sample to optimise the high-resolution scan in terms of decreasing the number of field-of-view (FOV) images required. This approach, while commonly applied in microscopy, has never been fully evaluated to quantify expected improvements in scan time when the high-resolution scans are optimised on each sample. In addition, the methods have potential to improve image quality by reducing both image stitching and focus tracking artefacts.
Multi-scale mass segmentation for mammograms via cascaded random forests

Breast mass detection and segmentation are difficult tasks due to the variation in size and shape of breast masses. Constructing classifiers for this problem is also challenging due to the fact that normal tissue regions overwhelmingly outnumber abnormal regions. In this paper, we propose a novel approach for detecting and segmenting breast masses in mammography based on multi-scale morphological filtering and a self-adaptive cascade of random forests (CasRFs). CasRFs can cope with severe class imbalance by adding layers to the cascade until a minimum number of false-positives (FPs) is reached. The approach achieves an average sensitivity of 0.94 with 1.99 FPs/image on INbreast and a sensitivity of 0.77 with 3.93 FPs/image on DDSM BCRP.
Towards Optimal Image Stitching for Virtual Microscopy

In this paper we present an image stitching method based on dynamic programming and describe its application to automated slide acquisition for Virtual Microscopy (VM). Given a large number of fields of view (FOVs) acquired from a single microscope slide, we composite these images into a single large ‘virtual slide’ image. The location of each FOV is determined using a new algorithm based on dynamic programming. We compare the performance of the proposed algorithm to an existing greedy algorithm. In a visual trial it is shown that the new algorithm provides a significant improvement in perceived image quality at image boundaries compared to the existing algorithm.
Fully automated classification of mammograms using deep residual neural networks

In this paper, we propose a multi-view deep residual neural network (mResNet) for the fully automated classification of mammograms as either malignant or normal/benign. Specifically, our mResNet approach consists of an ensemble of deep residual networks (ResNet), which have six input images, including the unregistered craniocaudal (CC) and mediolateral oblique (MLO) mammogram views as well as the automatically produced binary segmentation maps of the masses and micro-calcifications in each view. We then form the mResNet by concatenating the outputs of each ResNet at the second to last layer, followed by a final, fully connected, layer. The resulting mResNet is trained in an end-to-end fashion to produce a case-based mammogram classifier that has the potential to be used in breast screening programs. We empirically show on the publicly available INbreast dataset, that the proposed mResNet classifies mammograms into malignant or normal/benign with an AUC of 0.8.
Exploring the Biophysical Drivers of Amazon Phenology: Preparing Data Sets to Improve Dynamic Global Vegetation Models

We explore the relative influence of biophysical drivers on phenology to assist validation and parameterization of Dynamic Global Vegetation Models. Using 6.8 years of MODIS data we created a vegetation index time series to map the spatial variability of vegetation phenology in the Amazon. TRMM and CERES data were used as a measure of two biophysical variables, precipitation and net radiation respectively. Using a Fourier transform and cross spectral analysis two aspects were considered from these data, the coincidence of: (A) spatial patterns, presence and strength in the annual cycle, and (B) the coherency and phase differences between the phenology and the biophysical variables. Using the Amazon as a study area we find that the coincidence between phenology and the drivers in annual power strength was not linear and in an area of high coherency we found radiation and phenology was almost in phase, whilst precipitation was not. The correspondence of slightly subdued annual phenology with strong annual radiation indicated that other drivers also influence the strength of the phenology.
Mixed content image compression by gradient field integration

Sketch based image coding decomposes an input image into a piece-wise smooth approximation image and a residual image. Image compression by gradient field integration follows this model, but differs by generating the approximation image from gradient data along edge contours and regularly sampled low resolution image data. This allows direct and efficient calculation of an approximation image which is smooth between edges. In this paper, we describe the image compression by gradient field integration approach, together with a low complexity implementation intended for near visually lossless compression of mixed content images. The implementation uses gradient field integration by scanline convolution, simple edge data extraction, DCT residual image coding and is block-based; it is suited to compression of images containing sharp edges occurring along pixel borders. Compression results are provided indicating potential gains from this method.
Spikes from compound action potentials in simulated microelectrode recordings

In this paper we demonstrate by simulation, that the spike features apparent in low-impedance deep brain stimulation (DBS) targeting microelectrode recordings (MER) may not reflect the action potentials of individual neurons. Rather, they are more likely to be compound action potentials from a synchronized group of neurons local to the electrode. Initially we simulate the MER by combining the electric fields from a large number of independent neurons surrounding the microelectrode tip. When synchronization is introduced amongst neurons the resulting discernible spikes in an MER are far more likely to relate to compound action potentials from subsets of synchronized neurons than individual action potentials. Different sub-sets of neurons are then synchronized to see how well a conventional spike sorting algorithm can differentiate the compound action potentials from different groups of neurons. These simulations offer insight into the clinical interpretation of DBS MERs used to target deep brain structures.
Microscopic specimen delineation using auto-phase correlation index

Rapid and high-quality digital acquisition of microscopic specimens is critically dependent upon an initial low-resolution delineation of the specimen and the construction of a focus map to guide the subsequent high-resolution scan. In this paper, we propose a novel field-of-view (FOV) based algorithm that not only accurately delineates the specimen, but also highlights superior FOVs from which to build the focus map. Specifically, we evaluate two FOV evaluation metrics: one “baseline” method based on a threshold of intensity values in each FOV and the other based on the proposed auto-phase correlation index (APCI). While phase correlation is a well known measure of phase diversity, we show that APCI can be efficiently utilised as a metric to both delineate the specimen and highlight superior FOV focus candidates. The experimental results, on real-world cytology specimens, demonstrate that APCI is both effective and robust at specimen delineation, particularly in the presence of artifacts such as glue and dust. In addition, APCI is shown to be effective at selecting a subset of FOVs that can be correctly focused.
Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms

In this paper, we propose a new method for the segmentation of breast masses from mammograms using a conditional random field (CRF) model that combines several types of potential functions, including one that classifies image regions using deep learning. The inference method used in this model is the tree re-weighted (TRW) belief propagation, which allows a learning mechanism that directly minimizes the mass segmentation error and an inference approach that produces an optimal result under the approximations of the TRW formulation. We show that the use of these inference and learning mechanisms and the deep learning potential functions provides gains in terms of accuracy and efficiency in comparison with the current state of the art using the publicly available datasets INbreast and DDSM-BCRP.
Rule Extraction from Support Vector Machines: Measuring the Explanation Capability Using the Area under the ROC Curve

Recently, the area of rule extraction from support vector machines (SVMs) has been explored. One important indication of the success of a rule extraction method is the performance of extracted rules as compared to the original SVM. In this paper, we describe the use of the area under the receiver operating characteristics (ROC) curve (AUC) to assess the quality of rules extracted from an SVM. In particular, we directly compare AUC to the more commonly used measures of accuracy and fidelity and show that AUC is both a more reliable and meaningful measure to use
Over-sampling for accurate masking thershold calculation wavelet packet audio coders

Many existing audio coders use a critically sampled discrete wavelet transform (DWT) for the decomposition of audio signals. While the aliasing present in the wavelet coefficients is cancelled in the decoder, these coders normally perform calculation of the simultaneous masking threshold directly on these aliased coefficients. This paper uses over-sampling in the wavelet packet decomposition in order to provide alias-free coefficients for accurate simultaneous masking threshold calculation. The proposed technique is compared with masking threshold calculation based upon the FFT and critically-sampled wavelet coefficients, and the results show that a bit rate saving of up to 16 kbit/s can be achieved using over-sampling
Effect of competing stimuli on SSVEP-based BCI

Steady-state visual evoked potential (SSVEP)-based Brain-Computer Interface (BCI) works on the basis that an attended stimulus shows an enhanced visual evoked response. By examining EEG power at the frequency of the dominant evoked response, we are able to determine which stimulus the subject is attending. However, due to the limited processing capability of human visual system, when presented with multiple stimuli in the same visual field, the stimuli will compete for neural representations in the cortices. This study elucidates the effect of competing stimuli on SSVEP amplitudes by exploring the relationship between the number of stimuli and their inter-distance on the power spectra of attended stimuli. Results show that competing stimuli, when placed less than five degrees from the centre of the fovea, create a significant suppressive effect on the dominant frequency response. This result should guide how visual stimuli of SSVEP-based BCIs are spatially designed.
Rule Extraction from Support Vector Machines: A Sequential Covering Approach

In this paper, we propose a novel algorithm for rule extraction from support vector machines (SVMs), termed SQRex-SVM. The proposed method extracts rules directly from the support vectors (SVs) of a trained SVM using a modified sequential covering algorithm. Rules are generated based on an ordered search of the most discriminative features, as measured by interclass separation. Rule performance is then evaluated using measured rates of true and false positives and the area under the receiver operating characteristic (ROC) curve (AUC). Results are presented on a number of commonly used data sets that show the rules produced by SQRex-SVM exhibit both improved generalization performance and smaller more comprehensible rule sets compared to both other SVM rule extraction techniques and direct rule learning techniques.
Automatic Segmentation of Enhancing Breast Tissue in Dynamic Contrast-Enhanced MR Images

We present a novel method for the segmentation of enhancing breast tissue, suspicious of malignancy, in dynamic contrast-enhanced (DCE) MR images. The method is based on seeded region growing and merging using criteria based on both the original image intensity values and the fitted parameters of a novel empiric parametric model of contrast enhancement. We present the results of the application of the method to DCE-MRI data sets originating from breast MRI examinations of 24 subjects (10 cases of benign and 14 cases of malignant enhancement). The results show that the segmentation method has 100% sensitivity for the detection of suspicious regions independently identified by a radiologist. The results suggest that the method has potential both as a tool to assist the clinician with the task of locating suspicious tissue and as input to a computer assisted diagnostic system for generating quantitative features for automatic classification of suspicious tissue.