Paper proposes clustering model for ICC based on histologic patterns.
problem Challenges in grading rare cancers like ICC due to small sample sizes and difficulty in extracting patterns.
method Unsupervised deep convolutional autoencoder clustering model trained on 246 ICC digitized slides.
result Three clusters significantly associated with recurrence-free survival in Cox-proportional hazard models.
Context-aware CNN improves cancer grading accuracy.
problem Grading colorectal cancer histology images accurately.
method Proposes a context-aware neural network for 1,792x1,792 pixel images.
result Outperforms traditional methods by 3.61%.
Efficient and precise classification of histological cell nuclei is of utmost importance due to its potential applications in the field of medical image analysis. It would facilitate the medical practitioners to better understand and explore various factors for cancer treatment. The classification of histological cell …
We propose a patch sampling strategy based on a sequential Monte-Carlo method for high resolution image classification in the context of Multiple Instance Learning. When compared with grid sampling and uniform sampling techniques, it achieves higher generalization performance. We validate the strategy on two artificial…
Algorithm segments glandular structures in colon histology images for cancer grading.
problem Manual gland segmentation is time-consuming and risky for patients.
method Local intensity and texture features, Random Forest classifier, multilevel approach.
result Fast, accurate automatic gland segmentation for clinical use.
Stem uses diffusion models to infer gene expression from H&E images.
problem Inference of gene expression from H&E stained images is time-consuming and expensive.
method Conditional diffusion generative model to infer gene expression.
result Stem achieves state-of-the-art performance in spatial gene expression prediction.
Semi-supervised learning chain boosts histology image accuracy with minimal labeled data.
problem Expensive annotation of large histology image databases.
method Teacher-student knowledge distillation chain approach.
result Accuracy matches 100% labeled data with only 0.5% labeled data.
Deep learning model diagnoses celiac disease severity from intestinal biopsy images.
problem Diagnosing celiac disease severity from biopsy images, especially mild cases.
method Deep residual networks trained on a modified Marsh score histological scoring system.
result Model achieved AUC > 0.96 in all classes for CD severity classification.
Automatization of the diagnosis of any kind of disease is of great importance and it's gaining speed as more and more deep learning solutions are applied to different problems. One of such computer aided systems could be a decision support too able to accurately differentiate between different types of breast cancer hi…
Study uses deep learning to detect BCCs in high-res histopathological images.
problem Detecting BCCs in high-resolution, weakly labeled histopathological images.
method Attention-based deep learning models to process ultra-high resolution images with weak labels.
result Attention-based models achieve almost perfect classification performance (AUC of 0.99).
A deep learning framework separates overlapping nuclei in histology images.
problem Challenges in nuclear segmentation due to overlapping nuclei.
method Proposal-free spatially-aware deep learning framework with multi-scale spatial information.
result State-of-the-art performance in nuclear segmentation on a multi-organ data set.
This paper benchmarks OoDD methods for medical imaging.
problem Medical models trained for one domain may fail on images from a different domain.
method Defined 3 categories of OoD examples and benchmarked methods in 3 medical imaging domains.
result Simple binary classifier on feature representation yields best accuracy and AUPRC.
Histopathology tissue samples are widely available in two states: paraffin-embedded unstained and non-paraffin-embedded stained whole slide RGB images (WSRI). Hematoxylin and eosin stain (H&E) is one of the principal stains in histology but suffers from several shortcomings related to tissue preparation, staining proto…
Dropout improves MIL performance on noisy WSI classification.
problem Noisy feature embeddings and weak supervision in MIL for WSI classification.
method Empirical exploration of dropout in MIL, proposing MIL-Dropout.
result MIL-Dropout boosts MIL performance with minimal computational cost.
Universally valid ground truth is almost impossible to obtain or would come at a very high cost. For supervised learning without universally valid ground truth, a recommended approach is applying crowdsourcing: Gathering a large data set annotated by multiple individuals of varying possibly expertise levels and inferri…
A deep learning approach classifies medical images hierarchically.
problem Limitations of traditional supervised classifiers in medical image classification.
method Hierarchical Medical Image Classification (HMIC) using deep learning models.
result HMIC achieved better performance in classifying medical images hierarchically.
A new method for deep multiple instance learning using self-attention.
problem Classifying bags of instances with dependencies.
method Introducing Self-Attention-based aggregation operation for bags of instances.
result SA-AbMILP outperforms other models in various datasets.
Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the…
Safe Pattern Pruning reduces pattern explosion in predictive pattern mining.
problem Exponential growth of patterns in structured data.
method Safe Pattern Pruning (SPP) method.
result Effective model building in practical data analysis.
BN^2MF identifies unknown exposure patterns in environmental mixtures.
problem Identifying unknown exposure patterns in environmental mixtures.
method Bayesian non-parametric non-negative matrix factorization (BN^2MF) with non-negative continuous priors and a non-parametric sparse prior.
result Estimates patterns of chemical exposures without specifying the number of patterns.
RestoreAI predicts landmine risk from patterns, improving clearance efficiency.
problem Predicting landmine risk from spatial patterns to enhance clearance efficiency.
method RestoreAI uses landmine patterns for risk prediction, implementing three deminers: linear, curved, and Bayesian.
result RestoreAI significantly boosts clearance efficiency, achieving a 14.37 percentage point increase in cleared landmines per timestep.
Study of knots with generalized Mazur patterns and their invariants.
problem Understanding the invariants and properties of knots with generalized Mazur patterns.
method Computational analysis of τ and ε invariants for n-twisted satellites. result None of the n-twisted patterns from the family act surjectively on the smooth or rational concordance group. Paper proves rigidity of spherical ring patterns on surfaces.
problem Proving rigidity of spherical orthogonal ring patterns on closed surfaces.
method Modification of combinatorial total geodesic curvature and variational principles.
result Rigidity of spherical orthogonal ring patterns on closed surfaces proved.
Two approaches detect EV charging patterns at stations.
problem Identify charging patterns at electric vehicle charging stations.
method Two approaches: rule-based and hierarchical clustering.
result Hierarchical clustering revealed unexpected charging patterns.
CNN-based prostate cancer grading improves accuracy and efficiency.
problem Manual Gleason grading by pathologists is time-consuming and prone to errors.
method Patch-Based Image Reconstruction (PBIR), Distribution Correction (DC), Quadratic Weighted Mean Square Error (QWMSE).
result Achieved superior expert-level performance (0.8885 quadratic-weighted kappa coefficient).
Universal learning machine is a theory trying to study machine learning from mathematical point of view. The outside world is reflected inside an universal learning machine according to pattern of incoming data. This is subjective pattern of learning machine. In [2,4], we discussed subjective spatial pattern, and estab…
Proves existence of circle patterns on surfaces with cusps.
problem Existence of circle patterns with prescribed angles on surfaces with cusps.
method Introduced combinatorial Ricci and Calabi flows to prove longtime existence and convergence.
result Existence of generalized circle patterns with prescribed angles on surfaces with cusps.
Study of combinatorial Calabi flow on ideal circle patterns.
problem Finding ideal circle patterns with prescribed curvatures.
method Combinatorial Calabi flow in hyperbolic and Euclidean geometry.
result Flow converges exponentially to ideal circle patterns.
The paper studies circle patterns on surfaces with specific angles and curvature maps.
problem Investigating circle patterns with obtuse angles on surfaces of finite type.
method Characterizing curvature maps and establishing combinatorial Ricci flow conditions.
result Generalizations of circle pattern theorem and a computational method to find patterns.
CDPA identifies common and distinctive patterns in high-dimensional datasets.
problem Existing methods fail to capture the common pattern between coefficient matrices of shared latent factors.
method Proposes CDPA, an unsupervised learning method that incorporates both common and distinctive patterns of coefficient matrices.
result CDPA provides better characterization of common and distinctive patterns in high-dimensional datasets.
Study circle patterns on tori, linking symplectic forms and homeomorphisms.
problem Understanding circle patterns on tori and their symplectic properties.
method Investigates the space of circle patterns on closed tori with complex projective structures, embedding it into Teichmüller spaces and analyzing symplectic forms.
result Non-degeneracy of the pulled-back Weil-Petersson symplectic form and homeomorphism between circle patterns and Teichmüller spaces.
FSR efficiently discovers significant patterns with few resampled datasets.
problem Mining significant patterns in transactional data, especially subgroups.
method FSR uses resampling to bound the supremum deviation of quality statistics, providing rigorous guarantees on false discoveries.
result FSR effectively discovers significant subgroups with a small number of resampled datasets.
In this paper we study predictive pattern mining problems where the goal is to construct a predictive model based on a subset of predictive patterns in the database. Our main contribution is to introduce a novel method called safe pattern pruning (SPP) for a class of predictive pattern mining problems. The SPP method a…
Discovers discriminative patterns in two-class datasets.
problem Discovering patterns that occur more frequently in one class than the other.
method Proposes SSDPS algorithm with an original enumeration strategy exploiting anti-monotonicity.
result SSDPS outperforms other algorithms in terms of efficiency and pattern generation.
TFPS improves time series forecasting by learning pattern-specific experts.
problem Challenges in forecasting time series data with varying patterns across segments.
method Dual-domain encoder, subspace clustering, pattern-specific experts.
result Significantly improved forecasting accuracy, especially in long-term forecasting.
Pattern sampling has been proposed as a potential solution to the infamous pattern explosion. Instead of enumerating all patterns that satisfy the constraints, individual patterns are sampled proportional to a given quality measure. Several sampling algorithms have been proposed, but each of them has its limitations wh…
While Multiple Instance (MI) data are point patterns -- sets or multi-sets of unordered points -- appropriate statistical point pattern models have not been used in MI learning. This article proposes a framework for model-based MI learning using point process theory. Likelihood functions for point pattern data derived …
Generative model attacks CNN on MNIST by subtly replacing input patterns.
problem Adversarial attacks on neural networks.
method Generative model that replaces input patterns with generated ones.
result Demonstrated effectiveness on MNIST dataset.
The paper integrates statistical significance and discriminative power in pattern discovery.
problem Discovering actionable patterns that meet rigorous statistical significance and discriminative power criteria.
method Integrates statistical significance and discriminative power criteria into state-of-the-art algorithms.
result Improves discriminative power and statistical significance of discovered patterns without quality deterioration.
Paper extends circle pattern theory to obtuse angles.
problem Circle patterns with obtuse angles not previously covered.
method Using topological degree theory, extends Koebe-Andreev-Thurston Theorem.
result Generalized Andreev's Theorem for obtuse dihedral angles.
Pattern sampling reduces time series classification complexity.
problem High computational complexity of exhaustive search for shapelets.
method Pattern sampling using a weighted trie to extract discriminative patterns.
result Significant reduction in computational and memory resources.
Unified framework for linear attribution methods in deep learning.
problem Separate theoretical foundations of XAI attribution methods.
method GRALIS (Gradient-Riesz Averaged Locally-Integrated Shapley) framework.
result Unified representation theory for linear attribution methods.
Symplectic forms match on circle pattern space.
problem Matching symplectic forms on circle pattern space.
method Pullback of symplectic forms to circle pattern space.
result Symplectic forms on circle pattern space coincide.
Study identifies clusters of EU countries with similar young mortality patterns.
problem Identify clusters of EU countries with similar mortality patterns in young population.
method Symbolic data analysis (SDA) with age, gender, and main causes of death dimensions.
result Identified clusters of EU countries with similar mortality patterns in young population.
This research improves LSTM for monthly electricity demand forecasting using pattern-based methods.
problem Forecasting mid-term monthly electricity demand with high accuracy.
method Developed a hybrid LSTM model using x-patterns and exponential smoothing.
result The hybrid model outperformed standard LSTM and classical models.
Proposes SCR-Apriori for efficient mining of SCR-patterns.
problem Mining high-quality `Set of Contrasting Rules'-pattern (SCR-pattern) efficiently.
method Integrates SCR-pattern structure into Apriori algorithm to prune search space.
result Significantly reduces computational cost compared to state-of-the-art.
For using neural networks in safety critical domains, it is important to know if a decision made by a neural network is supported by prior similarities in training. We propose runtime neuron activation pattern monitoring - after the standard training process, one creates a monitor by feeding the training data to the ne…
In this paper, we investigate the multi-variate sequence classification problem from a multi-instance learning perspective. Real-world sequential data commonly show discriminative patterns only at specific time periods. For instance, we can identify a cropland during its growing season, but it looks similar to a barren…