Adaptive Variance Thresholding: A Novel Approach to Improve Existing Deep Transfer Vision Models and Advance Automatic Knee-Joint Osteoarthritis Classification

Knee-Joint Osteoarthritis (KOA) is a prevalent cause of global disability and is inherently complex to diagnose due to its subtle radiographic markers and individualized progression. One promising classification avenue involves applying deep learning methods; however, these techniques demand extensive, diversified datasets, which pose substantial challenges due to medical data collection restrictions. Existing practices typically resort to smaller datasets and transfer learning. However, this approach often inherits unnecessary pre-learned features that can clutter the classifier's vector space, potentially hampering performance. This study proposes a novel paradigm for improving post-training specialized classifiers by introducing adaptive variance thresholding (AVT) followed by Neural Architecture Search (NAS). This approach led to two key outcomes: an increase in the initial accuracy of the pre-trained KOA models and a 60-fold reduction in the NAS input vector space, thus facilitating faster inference speed and a more efficient hyperparameter search. We also applied this approach to an external model trained for KOA classification. Despite its initial performance, the application of our methodology improved its average accuracy, making it one of the top three KOA classification models.Comment: 26 pages, 5 figure

Synthesizing Bidirectional Temporal States of Knee Osteoarthritis Radiographs with Cycle-Consistent Generative Adversarial Neural Networks

Knee Osteoarthritis (KOA), a leading cause of disability worldwide, is challenging to detect early due to subtle radiographic indicators. Diverse, extensive datasets are needed but are challenging to compile because of privacy, data collection limitations, and the progressive nature of KOA. However, a model capable of projecting genuine radiographs into different OA stages could augment data pools, enhance algorithm training, and offer pre-emptive prognostic insights. In this study, we trained a CycleGAN model to synthesize past and future stages of KOA on any genuine radiograph. The model was validated using a Convolutional Neural Network that was deceived into misclassifying disease stages in transformed images, demonstrating the CycleGAN's ability to effectively transform disease characteristics forward or backward in time. The model was particularly effective in synthesizing future disease states and showed an exceptional ability to retroactively transition late-stage radiographs to earlier stages by eliminating osteophytes and expanding knee joint space, signature characteristics of None or Doubtful KOA. The model's results signify a promising potential for enhancing diagnostic models, data augmentation, and educational and prognostic usage in healthcare. Nevertheless, further refinement, validation, and a broader evaluation process encompassing both CNN-based assessments and expert medical feedback are emphasized for future research and development.Comment: 29 pages, 10 figure

Improving Performance in Colorectal Cancer Histology Decomposition using Deep and Ensemble Machine Learning

In routine colorectal cancer management, histologic samples stained with hematoxylin and eosin are commonly used. Nonetheless, their potential for defining objective biomarkers for patient stratification and treatment selection is still being explored. The current gold standard relies on expensive and time-consuming genetic tests. However, recent research highlights the potential of convolutional neural networks (CNNs) in facilitating the extraction of clinically relevant biomarkers from these readily available images. These CNN-based biomarkers can predict patient outcomes comparably to golden standards, with the added advantages of speed, automation, and minimal cost. The predictive potential of CNN-based biomarkers fundamentally relies on the ability of convolutional neural networks (CNNs) to classify diverse tissue types from whole slide microscope images accurately. Consequently, enhancing the accuracy of tissue class decomposition is critical to amplifying the prognostic potential of imaging-based biomarkers. This study introduces a hybrid Deep and ensemble machine learning model that surpassed all preceding solutions for this classification task. Our model achieved 96.74% accuracy on the external test set and 99.89% on the internal test set. Recognizing the potential of these models in advancing the task, we have made them publicly available for further research and development.Comment: 28 pages, 9 figure

Modeling friction between shearing brittle surfaces with a discrete element method

Hauraiden materiaalien murtuminen on eräs materiaalifysiikan peruskysymyksistä. Sille ei ole vieläkään olemassa täydellistä teoreettista selitystä, joten ennusteiden tekeminen nojaa vahvasti kokeisiin ja niihin perustuviin numeerisiin malleihin. Tässä työssä suunnittelemme ja toteutamme diskreettielementtimenetelmän sellaisen fysikaalisen systeemin tutkimiseen, jossa kaksi haurasta pintaa leikkaavat toisiaan. Keskitymme erityisesti siihen kuinka erilaiset voimamallit sisäiselle kitkalle vaikuttavat materiaalin ulkoiseen kitkaan. Käyttämällä graniittia koemateriaalina huomaamme, että sen ulkoinen kitka riippuu voimakkaasti sen sisäisestä kitkasta, kun ulkoinen paine on lähellä materiaalin puristuslujuutta. Muutoin ulkoisen kitkan määräävät pääasiallisesti leikkauspintojen karkeus tai niiden väliin muodostuneen sirpalekerroksen paksuus. Toisaalta, riippumatta ulkoisesta paineesta, sisäinen kitka vaikuttaa voimakkaasti sirpalekerroksen sisäiseen mekaniikkaan.The fracture of brittle materials is a fundamental problem in material physics. It still lacks a complete theoretical description, so making predictions relies heavily on experiments and numerical models based on them. In this work we design and implement a discrete element method to study the physical system of two brittle surfaces as they are sheared across each other. Our focus is specifically how different force models for internal friction affect the dry friction of the material. By using granite as our reference material, we find that the dry friction depends strongly on the internal friction when the pressure is around the ultimate strength of the material. Otherwise the dry friction is primarily determined by the roughness of the surfaces or the thickness of the fragment layer between the them. Still, regardless of the pressure, the presence of internal friction strongly influences the mechanics inside the fragment layer

Polkuintegraaliperustilamenetelmä

Tutustutaan lyhyesti polkuintegraaliperustilamenetelmään, joka on numeerinen satunnaislukumenetelmä kvanttimekaanisten monihiukkasjärjestelmien ominaisuuksien laskemiseen nollalämpötilassa. Ensiksi käydään läpi menetelmän syntyperä ja suhde muihin samankaltaisiin menetelmiin. Sitten tarkastellaan menetelmän taustalla vallitsevaa teoriaa lähtien kvanttimekaniikan ja tilastollisen fysiikan perustuksista. Teoriaosuuden pohjalta rakennetaan yksinkertainen mallitoteutus, jonka havainnollistamiseksi lasketaan kvanttiharmonisen oskillaattorin energia ja todennäköisyysjakauma sekä karkea arvio helium-4-nesteen parikorrelaatiofunktiolle. Lopuksi katsotaan vielä muutamia menetelmän olemassaolevia tai mahdollisia käyttökohteita.A short treatise is presented on the path integral ground state method, which is a numerical method for computing the properties of quantum many-body systems at zero temperature. First the origin of the method and its relation to other similar methods is reviewed. Then the underlying theory of the method is presented, starting from the foundations of quantum mechanics and statistical physics. Based on the presentation, a simple sample implementation is constructed and showcased by calculating the energy and probability density function of a quantum harmonic oscillator and a rough estimate for the pair correlation function of liquid helium-4. Finally some of the existing or potential applications of the method are examined

Curiously Empty Intersection of Proof Engineering and Computational Sciences

The tools and techniques of proof engineering have not yet been applied to the computational sciences. We try to explain why and investigate their potential to advance the field. More concretely, we formalize elementary group theory in an interactive theorem prover and discuss how the same technique could be applied to formalize general computational methods, such as discrete exterior calculus. We note that such formalizations could reveal interesting insights into the mathematical structure of the methods and help us implement them with stronger guarantees of correctness. We also postulate that working in this way could dramatically change the way we study and communicate computational sciences.peerReviewe

Exploring the Efficacy of Base Data Augmentation Methods in Deep Learning-Based Radiograph Classification of Knee Joint Osteoarthritis

Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive datasets. However, obtaining these datasets poses significant challenges due to patient privacy and data collection restrictions. Additive data augmentation, which enhances data variability, emerges as a promising solution. Yet, it’s unclear which augmentation techniques are most effective for KOA. Our study explored data augmentation methods, including adversarial techniques. We used strategies like horizontal cropping and region of interest (ROI) extraction, alongside adversarial methods such as noise injection and ROI removal. Interestingly, rotations improved performance, while methods like horizontal split were less effective. We discovered potential confounding regions using adversarial augmentation, shown in our models’ accurate classification of extreme KOA grades, even without the knee joint. This indicated a potential model bias towards irrelevant radiographic features. Removing the knee joint paradoxically increased accuracy in classifying early-stage KOA. Grad-CAM visualizations helped elucidate these effects. Our study contributed to the field by pinpointing augmentation techniques that either improve or impede model performance, in addition to recognizing potential confounding regions within radiographic images of knee osteoarthritis

Reflectance Measurement Method Based on Sensor Fusion of Frame-Based Hyperspectral Imager and Time-of-Flight Depth Camera

Hyperspectral imaging and distance data have previously been used in aerial, forestry, agricultural, and medical imaging applications. Extracting meaningful information from a combination of different imaging modalities is difficult, as the image sensor fusion requires knowing the optical properties of the sensors, selecting the right optics and finding the sensors’ mutual reference frame through calibration. In this research we demonstrate a method for fusing data from Fabry–Perot interferometer hyperspectral camera and a Kinect V2 time-of-flight depth sensing camera. We created an experimental application to demonstrate utilizing the depth augmented hyperspectral data to measure emission angle dependent reflectance from a multi-view inferred point cloud. We determined the intrinsic and extrinsic camera parameters through calibration, used global and local registration algorithms to combine point clouds from different viewpoints, created a dense point cloud and determined the angle dependent reflectances from it. The method could successfully combine the 3D point cloud data and hyperspectral data from different viewpoints of a reference colorchecker board. The point cloud registrations gained 0.29–0.36 fitness for inlier point correspondences and RMSE was approx. 2, which refers a quite reliable registration result. The RMSE of the measured reflectances between the front view and side views of the targets varied between 0.01 and 0.05 on average and the spectral angle between 1.5 and 3.2 degrees. The results suggest that changing emission angle has very small effect on the surface reflectance intensity and spectrum shapes, which was expected with the used colorchecker.peerReviewe

Systematisation of Systems Solving Physics Boundary Value Problems

A general conservation law that defines a class of physical field theories is constructed. First, the notion of a general field is introduced as a formal sum of differential forms on a Minkowski manifold. By the action principle the conservation law is defined for such a general field. By construction, particular field notions of physics, e.g., magnetic flux, electric field strength, stress, strain etc. become instances of the general field. Hence, the differential equations that constitute physical field theories become also instances of the general conservation law. The general field and the general conservation law together correspond to a large class of relativistic hyperbolic physical field models. The parabolic and elliptic models can thereafter be derived by adding constraints. The approach creates solid foundations for developing software systems for scientific computing; the unifying structure shared by the class of field models makes it possible to implement software systems which are not restricted to certain predefined problems. The versatility of the proposed approach is demonstrated by numerical experiments with moving and deforming domains.peerReviewe

Chromosome Xq23 is associated with lower atherogenic lipid concentrations and favorable cardiometabolic indices

Autosomal genetic analyses of blood lipids have yielded key insights for coronary heart disease (CHD). However, X chromosome genetic variation is understudied for blood lipids in large sample sizes. We now analyze genetic and blood lipid data in a high-coverage whole X chromosome sequencing study of 65,322 multi-ancestry participants and perform replication among 456,893 European participants. Common alleles on chromosome Xq23 are strongly associated with reduced total cholesterol, LDL cholesterol, and triglycerides (min P=8.5x10(-72)), with similar effects for males and females. Chromosome Xq23 lipid-lowering alleles are associated with reduced odds for CHD among 42,545 cases and 591,247 controls (P=1.7x10(-4)), and reduced odds for diabetes mellitus type 2 among 54,095 cases and 573,885 controls (P=1.4x10(-5)). Although we observe an association with increased BMI, waist-to-hip ratio adjusted for BMI is reduced, bioimpedance analyses indicate increased gluteofemoral fat, and abdominal MRI analyses indicate reduced visceral adiposity. Co-localization analyses strongly correlate increased CHRDL1 gene expression, particularly in adipose tissue, with reduced concentrations of blood lipids. The influence of X chromosome genetic variation on blood lipids and coronary heart disease (CHD) is not well understood. Here, the authors analyse X chromosome sequencing data across 65,322 multi-ancestry individuals, identifying associations of the Xq23 locus with lipid changes and reduced risk of CHD and diabetes mellitus.Peer reviewe