nils-herrmann/clean_scirepeval huggingface dataset:
Hyperspectral Colon Tissue Classification using Morphological Analysis

Diagnosis and cure of colon cancer can be improved by efficiently classifying the colon tissue cells into normal and malignant classes. This paper presents the classification of hyperspectral colon tissue cells using morphological analysis of gland nuclei cells. The application of hyperspectral imaging technique in medical image analysis is a new domain for researchers. The main advantage in using hyperspectral imaging is the increased spectral resolution and detailed subpixel information. Biopsy slides with several microdots, where each microdot is from a distinct patient, are illuminated with a tuned light source and magnification is performed up to 400times. The proposed classification algorithm combines the hyperspectral imaging technique with linear discriminant analysis. Dimensionality reduction and cellular segmentation is achieved by independent component analysis (ICA) and k-means clustering. Morphological features, which describe the shape, orientation and other geometrical attributes, are next to be extracted. For classification, LDA is employed to discriminate tissue cells into normal and malignant classes. Implementation of LDA is simpler than other approaches; it saves the computational cost, while maintaining the performance. The algorithm is tested on a number of samples and its applicability is demonstrated with the help of measures such as classification accuracy rate and the area under the convex hull of ROC curves
A model of the spatial microenvironment of the colonic crypt

There have been great advancements in the field of immunofluorescence imaging. The surge in development of analytical methods for such data makes it crucial to develop benchmark synthetic datasets for objectively validating these methods. We propose a model of the healthy colonic crypt microenvironments. Our model can simulate immunofluorescence image data with parameters that allow control over cellularity, cell overlap ratio, image resolution, and objective level. To the best of our knowledge, ours is the first model to simulate immunofluorescence image data at subcellular level for healthy colon tissue, where the cells have several compartments and are organized to mimic the microenvironment of tissue in situ rather than dispersed cells in a cultured environment. Validation of the model has been performed by comparing morphological features of the tissue structure between real and simulated images. In addition, we compare the performance of two cell counting algorithms. The simulated data could also be used to validate techniques such as image restoration, cell segmentation, and crypt segmentation.
SAMS-NET: Stain-aware multi-scale network for instance-based nuclei segmentation in histology images

Segmentation of nuclear material in histology slides is an important step in the digital pathology work-flow, due to the ability for nuclei to act as key diagnostic markers. Manual segmentation can be a laborious task, where pathologists are often required to analyse many nuclei within a whole slide image (WSI). The rise in digital pathology has been matched with an increase in interest for automated nuclei segmentation in Hematoxylin & Eosin (H&E) stained histology images, yet this remains a challenge due to the heterogeneous appearance of different types of nuclei. This heterogeneity can lead to nuclei having a variable Hematoxylin intensity, which often has detrimental effects on the success of current methods. We propose a deep multi-scale neural network, with a novel loss function that is sensitive to the Hematoxylin intensity, for precise object-level nuclei segmentation. We show that the proposed network outperforms all competing methods for the computational precision medicine (CPM) nuclei segmentation challenge dataset as part of MICCAI 2017.
Image denoising using multiscale directional cosine bases

Motivated by the fact that in images, there is usually a presence of local strongly oriented harmonics, a representation which is both well-localised in frequency and orientation is desirable to efficiently describe such oriented harmonic features. Here we introduce a family of multiscale trigonometric bases for a bi-variate function called the multiscale directional cosine bases for image denoising tasks. Our results show the promise of the new bases which almost consistently outperform other image representation bases on natural images.
Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images

Detection and classification of cell nuclei in histopathology images of cancerous tissue stained with the standard hematoxylin and eosin stain is a challenging task due to cellular heterogeneity. Deep learning approaches have been shown to produce encouraging results on histopathology images in various studies. In this paper, we propose a Spatially Constrained Convolutional Neural Network (SC-CNN) to perform nucleus detection. SC-CNN regresses the likelihood of a pixel being the center of a nucleus, where high probability values are spatially constrained to locate in the vicinity of the centers of nuclei. For classification of nuclei, we propose a novel Neighboring Ensemble Predictor (NEP) coupled with CNN to more accurately predict the class label of detected cell nuclei. The proposed approaches for detection and classification do not require segmentation of nuclei. We have evaluated them on a large dataset of colorectal adenocarcinoma images, consisting of more than 20,000 annotated nuclei belonging to four different classes. Our results show that the joint detection and classification of the proposed SC-CNN and NEP produces the highest average F1 score as compared to other recently published approaches. Prospectively, the proposed methods could offer benefit to pathology practice in terms of quantitative analysis of tissue constituents in whole-slide images, and potentially lead to a better understanding of cancer.
A random polygons model of glandular structures in colon histology images

In this paper, we present a stochastic model for glandular structures in Hematoxylin and Eosin stained histology images, choosing colon tissue as an example. The proposed Random Polygons Model (RPM) treats each glandular structure in an image as a polygon made of a random number of vertices, where the vertices represent approximate locations of epithelial nuclei. We formulate the RPM as a Bayesian inference problem by defining a prior for spatial connectivity and arrangement of neighboring epithelial nuclei and likelihood about the presence of glandular structure. The inference is made via a Reversible-Jump Markov Chain Monte Carlo simulation. Our experimental results show that the RPM yields favorable results, both quantitatively and qualitatively, for extraction of glandular regions in histology images of human colon tissue.
Hybrid deep autoencoder with Curvature Gaussian for detection of various types of cells in bone marrow trephine biopsy images

Automated cell detection is a critical step for a number of computer-assisted pathology related image analysis algorithm. However, automated cell detection is complicated due to the variable cytomorphological and histological factors associated with each cell. In order to efficiently resolve the challenge of automated cell detection, deep learning strategies are widely applied and have recently been shown to be successful in histopathological images. In this paper, we concentrate on bone marrow trephine biopsy images and propose a hybrid deep autoencoder (HDA) network with Curvature Gaussian model for efficient and precise bone marrow hematopoietic stem cell detection via related high-level feature correspondence. The accuracy of our proposed method is up to 94%, outperforming other supervised and unsupervised detection approaches.
How divided is a cell? Eigenphase nuclei for classification of mitotic phase in cancer histology images

Detection of mitotic cells in histology images is an important but challenging process due to the resemblance of mitotic cells with other non-mitotic cells and also due to the different appearance of mitotic cells undergoing different phases of the division process. In this paper, we present an algorithm for classification of mitotic cells into its four different phases using eigenphase nuclei images - nuclear exemplars obtained separately from the eigen-decomposition of training nuclei images belonging to each of the four mitotic phases. To the best of our knowledge, ours is the first method to identify mitotic phases in cancer histology images. It is quite likely that the classification results may be negatively affected if the dataset used for training purposes does not contain sufficient number of samples for a positive class. To overcome this class imbalance problem, we present a novel method for oversampling the minority class. The proposed method generates synthetic images for training purposes by perturbing the representation of training samples belonging to the minority class in the eigenphase domain. We show that this strategy works effectively for pairwise classification of the mitotic cells - increasing the classification performance by as much as 24%.
Dual-Channel Active Contour Model for Megakaryocytic Cell Segmentation in Bone Marrow Trephine Histology Images

Assessment of morphological features of megakaryocytes (MKs) (special kind of cells) in bone marrow trephine biopsies play an important role in the classification of different subtypes of Philadelphia-chromosome-negative myeloproliferative neoplasms (Ph-negative MPNs). In order to aid hematopathologists in the study of MKs, we propose a novel framework that can efficiently delineate the nuclei and cytoplasm of these cells in digitized images of bone marrow trephine biopsies. The framework first employs a supervised machine learning approach that utilizes color and texture features to delineate megakaryocytic nuclei. It then employs a novel dual-channel active contour model to delineate the boundary of megakaryocytic cytoplasm by using different deconvolved stain channels. Compared to other recent models, the proposed framework achieves accurate results for both megakaryocytic nuclear and cytoplasmic delineation.
Wavelets and support vector machines for texture classification

We present a novel texture classification algorithm using 2-D discrete wavelet transform (DWT) and support vector machines (SVM). The DWT is used to generate feature images from individual wavelet subbands, and a local energy function is computed corresponding to each pixel of the feature images. This feature vector is first used for training and later on for testing the SVM classifier. The experimental setup consists of images from the Brodatz and MlT VisTeX texture databases and a combination of some images therein. The proposed method produces promising classification results for both single and multiple class texture analysis problems.
Flexible synapse detection in fluorescence micrographs by modeling human expert grading

A particularly difficult task in molecular imaging is the analysis of fluorescence microscopy images of neural tissue, as they usually exhibit a high density of objects with diffuse signals. To automate synapse detection in such images, one has to simulate aspects of human pattern recognition skills to account for low signal-to-noise-ratios. We propose a machine learning based method that allows a direct integration of the experts' visual expertise who tag a low number of referential synapses according to their degree of synapse likeness. The sensitivity and positive predictive values show that by using graded likeness information in our learning algorithm we can provide an intuitively tunable tool for neural tissue slide evaluation.
On generating cell exemplars for detection of mitotic cells in breast cancer histopathology images

Mitotic activity is one of the main criteria that pathologists use to decide the grade of the cancer. Computerised mitotic cell detection promises to bring efficiency and accuracy into the grading process. However, detection and classification of mitotic cells in breast cancer histopathology images is a challenging task because of the large intra-class variation in the visual appearance of mitotic cells in various stages of cell division life cycle. In this paper, we test the hypothesis that cells in histopathology images can be effectively represented using cell exemplars derived from sub-images of various kinds of cells in an image for the purposes of mitotic cell classification. We compare three methods for generating exemplar cells. The methods have been evaluated in terms of classification performance on the MITOS dataset. The experimental results demonstrate that eigencells combined with support vector machines produce reasonably high detection accuracy among all the methods.
Histopathological Image Analysis: A Review

Over the past decade, dramatic increases in computational power and improvement in image analysis algorithms have allowed the development of powerful computer-assisted analytical approaches to radiological data. With the recent advent of whole slide digital scanners, tissue histopathology slides can now be digitized and stored in digital image form. Consequently, digitized tissue histopathology has now become amenable to the application of computerized image analysis and machine learning techniques. Analogous to the role of computer-assisted diagnosis (CAD) algorithms in medical imaging to complement the opinion of a radiologist, CAD algorithms have begun to be developed for disease detection, diagnosis, and prognosis prediction to complement the opinion of the pathologist. In this paper, we review the recent state of the art CAD technology for digitized histopathology. This paper also briefly describes the development and application of novel image analysis technology for a few specific histopathology related problems being pursued in the United States and Europe.
Using Geodesic Space Density Gradients for Network Community Detection

Many real world complex systems naturally map to network data structures instead of geometric spaces because the only available information is the presence or absence of a link between two entities in the system. To enable data mining techniques to solve problems in the network domain, the nodes need to be mapped to a geometric space. We propose this mapping by representing each network node with its geodesic distances from all other nodes. The space spanned by the geodesic distance vectors is the geodesic space of that network. The position of different nodes in the geodesic space encode the network structure. In this space, considering a continuous density field induced by each node, density at a specific point is the summation of density fields induced by all nodes. We drift each node in the direction of positive density gradient using an iterative algorithm till each node reaches a local maximum. Due to the network structure captured by this space, the nodes that drift to the same region of space belong to the same communities in the original network. We use the direction of movement and final position of each node as important clues for community membership assignment. The proposed algorithm is compared with more than 10 state-of-the-art community detection techniques on two benchmark networks with known communities using Normalized Mutual Information criterion. The proposed algorithm outperformed these methods by a significant margin. Moreover, the proposed algorithm has also shown excellent performance on many real-world networks.
Quantifying Spatial and Angular Resolution of Light-Field 3-D Displays

Light-field 3-D displays are expected to play an important role as terminal devices, visualizing 3-D objects apparently floating in the air, or letting viewers see through a window with a scene behind it. Currently, there are neither methods nor practical tools to quantify light-field display's effective resolution or the perceived quality of the presented imagery. Most 3-D displays are simply characterized by the total number of pixels or light rays; however, this number does not properly characterize the distribution of the emitted light rays, or the level of detail that the display can visualize properly. This paper presents methods to measure the spatial (i.e., 2-D equivalent) and angular (i.e., directional) resolution of a given light-field display. The frequency-domain analysis of recorded test patterns gives the spatial resolution limit of the display under test, while angular resolution is determined by the display's ability to pass through patterns of uniform angle-dependent patterns. It also presents a subjective experiment to corroborate the objectively measured spatial resolution.
3D-DCT based perceptual quality assessment of stereo video

In this paper, we present a novel stereoscopic video quality assessment method based on 3D-DCT transform. In our approach, similar blocks from left and right views of stereoscopic video frames are found by block-matching, grouped into 3D stack and then analyzed by 3D-DCT. Comparison between reference and distorted images are made in terms of MSE calculated within the 3D-DCT domain and modified to reflect the contrast sensitive function and luminance masking. We validate our quality assessment method using test videos annotated with results from subjective tests. The results show that the proposed algorithm outperforms current popular metrics over a wide range of distortion levels.
Binocular depth perception: Does head parallax help people see better in depth?

In this work we study the limits of stereoscopic depth perception on an observer tracking 3D display, which provides head parallax. We perform two experiments where human participants are asked to observe two random dot sinusoidal gratings and choose the less “groovy” one. We use gratings with various amplitude and lateral wavelength, and visualize them with and without head parallax. In each experiment we measure the error rate, decision time and head movements. We present comparative results, which show how these parameters are affected by the presence of head parallax.
De-noising of distance maps sensed by time-of-flight devices in poor sensing environment

We propose a non-local de-noising approach aimed at filtering range data sensed by Photonic Mixer Device sensors. We address specifically the case of poor sensing environment when the reflected signal amplitude is low. In our approach, signal components of phase-delay and amplitude of the sensed signal are regarded as components of a complex-valued variable and processed together in a single step. This imposes better filter adaptivity and similarity weighting. The complex-domain filtering provides additional feedback in the form of improved noise-level confidence, which can be utilized in iterative de-noising schemes. Pre-filtering of individual components is proposed to suppress structural artifacts. Our approach compares favorably with state of the art approaches.
Efficient Super-Resolution Reconstruction for Translational Motion using a Near Least Squares Resampling Method

In this paper we propose a computationally efficient method for image super-resolution reconstruction. We concentrate on pure translational motion and shift-invariant blur. This is the case of interlaced sampling where each low resolution image is uniformly sampled but the overall sampling is non-uniform with respect to the high resolution grid. The reconstruction problem is considered in a multi-resolution framework using separable B-spline wavelets as basis. This allows performing the reconstruction on a uniform higher resolution grid by digital filtering. A computationally efficient structure-the transposed modified Farrow structure is used to achieve a near least squares solution without using any matrix inversions or iterations. The reconstruction can also be performed at non-dyadic scales. Our method minimizes the aliasing distortions and gives results comparable with other least squares techniques.
Reconstruction of Scalar Diffraction Field from Distributed Data Points Over 3D Space

Diffraction field computation is an important task in the signal conversion stage of the holographic 3DTV. We consider an abstract setting, where the diffraction field of the desired 3D scene to be displayed is given by discrete samples distributed over 3D space. Based on these samples, a model of the diffraction field should be built to allow the field computation at any desired point. In our previous works, we have proved our concepts for the simplistic 2D case. In this paper, we generalize the earlier proposed techniques, namely the projection onto convex sets and conjugate gradient based techniques and test them for their computational efficiency and memory requirements for a specific 3D case.
Tree-structured algorithm for efficient shearlet-domain light field reconstruction

This article considers techniques for accelerating a light field reconstruction algorithm operating in shearlet domain. In the proposed approach, an independent reconstruction of epipolar images (EPIs) is replaced with a consecutive tree-structured reconstruction. It aims at decreasing the number of iterations necessary for an EPI reconstruction by using already processed EPIs as initial values in the reconstruction stage. Two algorithms for structuring such processing trees are presented. The reconstruction performance of the proposed algorithms is illustrated on a real dataset. The underlying differences between the algorithms are discussed and numerical results of computation speeds are presented.
Regularized reconstruction of irregularly sampled scalar light fields

This paper addresses the problem of reconstruction of a monochromatic light field based on data points, irregularly distributed within a volume of interest. Such set up is considered to serve a wide area of applications related with three-dimensional display and beam shaping, where physically inconsistent input data is commonly available. Finite-dimensional models of scalar light fields are used to state the reconstruction problem as matrix inversion. Regularized inversion is done by the Tikhonov method, implemented by the iterative algorithm of conjugate gradients. The problem proves to be ill-posed, where the data points inconsistency is fully compensated by the regularized inversion, showing to be attractive for any application.
Time-of-Flight Range Measurement in Low-Sensing Environment: Noise Analysis and Complex-Domain Non-Local Denoising

In this paper, we deal with the problem of denoising 3D scene range measurements acquired by time-of-flight (ToF) range sensors and composed in the form of 2D image-like depth maps. We address the specific case of ToF low-sensing environment (LSE). Such environment is set by low-light sensing conditions, low-power hardware requirements, and low-reflectivity scenes. We demonstrate that data captured by a device in such mode can be effectively post-processed in order to reach the same measurement accuracy as if the device was working in normal operating mode. In order to achieve this, we first present an elaborated analysis of noise properties of ToF data sensed in LSE and verify the derived noise models by empirical measurements. Then, we develop a related novel non-local denoising approach working in complex domain and demonstrate its superiority against the state of the art for data acquired by an off-the-shelf ToF device.