The primary objective of the AIIB 2023 competition is to evaluate the predictive significance of airway-related imaging biomarkers in determining the survival outcomes of patients with lung fibrosis.This study introduces a comprehensive three-stage approach. Initially, a segmentation network, namely nn-Unet, is employed to delineate the airway's structural boundaries. Subsequently, key features are extracted from the radiomic images centered around the trachea and an enclosing bounding box around the airway. This step is motivated by the potential presence of critical survival-related insights within the tracheal region as well as pertinent information encoded in the structure and dimensions of the airway. Lastly, radiomic features obtained from the segmented areas are integrated into an SVM classifier. We could obtain an overall-score of 0.8601 for the segmentation in Task 1 while 0.7346 for the classification in Task 2.

Diffusion models have emerged as highly effective techniques for inpainting, however, they remain constrained by slow sampling rates. While recent advances have enhanced generation quality, they have also increased sampling time, thereby limiting scalability in real-world applications. We investigate the generative sampling process of diffusion-based inpainting models and observe that these models make minimal use of the input condition during the initial sampling steps. As a result, the sampling trajectory deviates from the data manifold, requiring complex synchronization mechanisms to realign the generation process. To address this, we propose Time-aware Diffusion Paint (TD-Paint), a novel approach that adapts the diffusion process by modeling variable noise levels at the pixel level. This technique allows the model to efficiently use known pixel values from the start, guiding the generation process toward the target manifold. By embedding this information early in the diffusion process, TD-Paint significantly accelerates sampling without compromising image quality. Unlike conventional diffusion-based inpainting models, which require a dedicated architecture or an expensive generation loop, TD-Paint achieves faster sampling times without architectural modifications. Experimental results across three datasets show that TD-Paint outperforms state-of-the-art diffusion models while maintaining lower complexity.

This study proposes a novel semi-supervised multi-label emotion classification approach for French tweets based on pseudo-labeling. Human subjectivity in emotional expression makes it difficult for a machine to learn. Therefore, it necessitates training supervised learning models on large datasets annotated by multiple annotators. However, creating such datasets can be costly and time-consuming. Moreover, aggregating annotations from multiple annotators to capture their collective emotional state adds complexity to the task. Semi-supervised learning techniques have shown effectiveness with limited datasets. Furthermore, Large language Models (LLMs), particularly Chat-GPT, have demonstrated superior annotation accuracy compared to annotations obtained from crowdsourcing platforms, when both are evaluated against expert-annotated data. This work introduces a novel approach for multi-label emotion classification of French tweets by leveraging pseudo-labels generated through Chat-GPT, a robust large language model. Using zero-shot, one-shot, and few-shot learning techniques, Chat-GPT annotates the unlabelled part of our dataset. These Chat-GPT-annotated pseudo-labels are then merged with manual annotations, facilitating the training of a multi-label emotion classification model via semi-supervised learning. Furthermore, within the context of our research, we present a new French tweet dataset, containing testimonials from people affected by an urban industrial incident. This dataset features 2,350 tweets, each manually annotated by three human annotators based on 8 pre-identified emotions. Benchmark results are presented for multi-label emotion classification models employing both fully supervised and semi-supervised approaches with pseudo-labeling. Our findings demonstrate the superiority of our approach for multi-label emotion classification over standard pseudo-labeling and an established baseline.

High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning. From medical images to text processing, traditional machine learning algorithms are usually unsuccessful in learning the best possible concept from such data. In a previous work, we proposed a dissimilarity-based approach for multi-view classification, the random forest dissimilarity, that perfoms state-of-the-art results for such problems. In this work, we transpose the core principle of this approach to solving HDLSS classification problems, by using the RF similarity measure as a learned precomputed SVM kernel (RFSVM). We show that such a learned similarity measure is particularly suited and accurate for this classification context. Experiments conducted on 40 public HDLSS classification datasets, supported by rigorous statistical analyses, show that the RFSVM method outperforms existing methods for the majority of HDLSS problems and remains at the same time very competitive for low or non-HDLSS problems.

In this paper, we study the problem of multi-domain translation: given an element (a) of domain A, we wish to generate a corresponding element (b) in another domain B, and vice versa. Acquiring supervision in multiple domains can be a tedious task, also we propose to learn this translation from one domain to another when supervision is available as a pair (a, b), and leverage possible unpaired data when only (a) or only (b) is available. We introduce a new unified framework called Latent Space Mapping (LSM), which exploits the manifold assumption to learn a latent space from each domain. Unlike existing approaches, we propose to further regularize each latent space using available domains by learning each dependency between pairs of domains. We evaluate our approach on three tasks performing i) a synthetic dataset with image translation, ii) a real-world task of semantic segmentation for medical images, and iii) a real-world task of facial landmark detection.

Domain-to-domain translation involves generating a target domain sample given a condition in the source domain. Most existing methods focus on fixed input and output domains, i.e. they only work for specific configurations (i.e. for two domains, either $D_1\rightarrow{}D_2$ or $D_2\rightarrow{}D_1$). This paper proposes Multi-Domain Diffusion (MDD), a conditional diffusion framework for multi-domain translation in a semi-supervised context. Unlike previous methods, MDD does not require defining input and output domains, allowing translation between any partition of domains within a set (such as $(D_1, D_2)\rightarrow{}D_3$, $D_2\rightarrow{}(D_1, D_3)$, $D_3\rightarrow{}D_1$, etc. for 3 domains), without the need to train separate models for each domain configuration. The key idea behind MDD is to leverage the noise formulation of diffusion models by incorporating one noise level per domain, which allows missing domains to be modeled with noise in a natural way. This transforms the training task from a simple reconstruction task to a domain translation task, where the model relies on less noisy domains to reconstruct more noisy domains. We present results on a multi-domain (with more than two domains) synthetic image translation dataset with challenging semantic domain inversion.

Multi-view learning is a learning task in which data is described by several concurrent representations. Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task. This is a challenge that can be met nowadays if there is a large amount of data available for learning. However, this is not necessarily true for all real-world problems, where data are sometimes scarce (e.g. problems related to the medical environment). In these situations, an effective strategy is to use intermediate representations based on the dissimilarities between instances. This work presents new ways of constructing these dissimilarity representations, learning them from data with Random Forest classifiers. More precisely, two methods are proposed, which modify the Random Forest proximity measure, to adapt it to the context of High Dimension Low Sample Size (HDLSS) multi-view classification problems. The second method, based on an Instance Hardness measurement, is significantly more accurate than other state-of-the-art measurements including the original RF Proximity measurement and the Large Margin Nearest Neighbor (LMNN) metric learning measurement.

Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions. For such tasks, dissimilarity strategies are effective ways to make the different descriptions comparable and to easily merge them, by (i) building intermediate dissimilarity representations for each view and (ii) fusing these representations by averaging the dissimilarities over the views. In this work, we show that the Random Forest proximity measure can be used to build the dissimilarity representations, since this measure reflects similarities between features but also class membership. We then propose a Dynamic View Selection method to better combine the view-specific dis-similarity representations. This allows to take a decision, on each instance to predict, with only the most relevant views for that instance. Experiments are conducted on several real-world multi-view datasets, and show that the Dynamic View Selection offers a significant improvement in performance compared to the simple average combination and two state-of-the-art static view combinations.

•Propose a Random forest dissimilarity based method for multi-view learning.•Study the effect of hyperparameters on the quality of random forest dissimilarity.•Compare the proposed method to the state of art Radiomics solutions.•Compare the proposed method to multi-view learning approaches•Show that the proposed approach outperforms the state-of-the-art methods.

Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past few years to be promising for achieving this personalization. However, a recent study shows that most of the state-of-the-art works in Radiomics fail to identify this problem as a multi-view learning task and that multi-view learning techniques are generally more efficient. In this work, we propose to further investigate the potential of one family of multi-view learning methods based on Multiple Classifier Systems where one classifier is learnt on each view and all classifiers are combined afterwards. In particular, we propose a random forest based dynamic weighted voting scheme, which personalizes the combination of views for each new patient to classify. The proposed method is validated on several real-world Radiomics problems.

Breast cancer is one of the most common types of cancer and leading cancer-related death causes for women. In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning. We find out that the deep learning networks pretrained on ImageNet have better performance than the popular handcrafted features used for breast cancer histology images. The best feature extractor achieves an average accuracy of 79.30%. To improve the classification performance, a random forest dissimilarity based integration method is used to combine different feature groups together. When the five deep learning feature groups are combined, the average accuracy is improved to 82.90% (best accuracy 85.00%). When handcrafted features are combined with the five deep learning feature groups, the average accuracy is improved to 87.10% (best accuracy 93.00%).

Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information. Many recent studies have proved that radiomics can offer a lot of useful information that physicians cannot extract from the medical images and can be associated with other information like gene or protein data. However, most of the classification studies in radiomics report the use of feature selection methods without identifying the machine learning challenges behind radiomics. In this paper, we first show that the radiomics problem should be viewed as an high dimensional, low sample size, multi view learning problem, then we compare different solutions proposed in multi view learning for classifying radiomics data. Our experiments, conducted on several real world multi view datasets, show that the intermediate integration methods work significantly better than filter and embedded feature selection methods commonly used in radiomics.

Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients. Radiomics is a recent medical imaging field that has shown during the past few years to be promising for achieving this personalization. However, we have shown in a recent study that most of the state-of-the-art works in Radiomics fail to identify this problem as a multi-view learning task and that multi-view learning techniques are generally more efficient. In this work, we propose to further investigate the potential of one family of multi-view learning methods based on Multiple Classifiers Systems where one classifier is learnt on each view and all classifiers are combined afterwards. In particular, we propose a random forest based dynamic weighted voting scheme, which personalizes the combination of views for each new patient for classification tasks. The proposed method is validated on several real-world Radiomics problems, with a comparison to the state-of-the-art Radiomics approach and to static voting schemes for Multiple Classifiers Systems.

In many real-world classification tasks, it is crucial to take into account misclassification costs for designing an accurate classification system. Nevertheless, begin able to reject a sample is also often needed in order to avoid a very risky prediction error. In that case, a cost-sensitive classifier must embed a rejection mechanism, that takes into account the rejection costs as well as the misclassification costs. In binary classification, the ROC space has shown to be very powerful for designing cost-sensitive classifiers, but it has been poorly exploited for designing classifiers able to reject. The purpose of this work is to extend a ROC-based ensemble method recently proposed, called the ROC Front method, with a cost-sensitive rejection mechanism. This approach compares favorably to the state-of-the-art ROC-based rejection rule recently proposed for binary cost-sensitive classification. It is also more robust as it allows to design an accurate classifier for all cost- sensitive situations contrary to the state-of-the-art method that fails in many cases, as for example with small datasets.

This paper addresses the problem of learning a multiclass classification system that can suit to any environment. By that we mean that particular (imbalanced) misclassification costs are taken into account by the classifier for predictions. However, these costs are not well known during the learning phase in most cases, or may evolve afterwards. There is a need in that case to learn a classifier that can potentially suit to any of these costs in prediction phase. The learning method proposed in this work, named the Multiclass ROC Front (MROCF) method, responds to this issue by exploiting ROC-based tools through a multiobjective optimization process. While this type of ROC-based multiobjective optimization approach has been successfully used for two-class problems, it has never been proposed in real-world multiclass classification problems. Experiments led on several real-world datasets show that the MROCF method offers a major improvement over a cost-insensitive classifier and is competitive with the state-of-the-art cost-sensitive optimization method on all but one of the 20 datasets.

Random Forest is an ensemble supervised machine learning technique. Machine learning techniques have applications in the area of Data mining. Random Forest has tremendous potential of becoming a popular technique for future classifiers because its performance has been found to be comparable with ensemble techniques bagging and boosting. Hence, an in-depth study of existing work related to Random Forest will help to accelerate research in the field of Machine Learning. This paper presents a systematic survey of work done in Random Forest area. In this process, we derived Taxonomy of Random Forest Classifier which is presented in this paper. We also prepared a Comparison chart of existing Random Forest classifiers on the basis of relevant parameters. The survey results show that there is scope for improvement in accuracy by using different split measures and combining functions; and in performance by dynamically pruning a forest and estimating optimal subset of the forest. There is also scope for evolving other novel ideas for stream data and imbalanced data classification, and for semi-supervised learning. Based on this survey, we finally presented a few future research directions related to Random Forest classifier.

One class classification is a binary classification task for which only one class of samples is available for learning. In some preliminary works, we have proposed One Class Random Forests (OCRF), a method based on a random forest algorithm and an original outlier generation procedure that makes use of classifier ensemble randomization principles. In this paper, we propose an extensive study of the behavior of OCRF, that includes experiments on various UCI public datasets and comparison to reference one class namely, Gaussian density models, Parzen estimators, Gaussian mixture models and One Class SVMs-with statistical significance. Our aim is to show that the randomization principles embedded in a random forest algorithm make the outlier generation process more efficient, and allow in particular to break the curse of dimensionality. One Class Random Forests are shown to perform well in comparison to other methods, and in particular to maintain stable performance in higher dimension, while the other algorithms may fail.

In this paper, we address the problem of one-class classification for medical image classification. Indeed, in some situations, pathological samples may be difficult to acquire. In this case, one class classification (OCC) is a natural learning paradigm to be used. It consists in learning from only one class of objects, while two or more classes may be presented in prediction. We propose an original OCC method called One-Class Random Forest (OCRF), that combines ensemble learning principles from traditional Random Forest algorithm with an original outlier generation method. These two key processes complement each other for responding to OCC issues, and are shown to perform well on medical datasets in comparison to few other state-of-the-art OCC methods.

In this paper, we introduce a new Random Forest (RF) induction algorithm called Dynamic Random Forest (DRF) which is based on an adaptative tree induction procedure. The main idea is to guide the tree induction so that each tree will complement as much as possible the existing trees in the ensemble. This is done here through a resampling of the training data, inspired by boosting algorithms, and combined with other randomization processes used in traditional RF methods. The DRF algorithm shows a significant improvement in terms of accuracy compared to the standard static RF induction algorithm.

We propose a new one-class classification method, called One Class Random Forest, that is able to learn from one class of samples only. This method, based on a random forest algorithm and an original outlier generation procedure, makes use of the ensemble learning mechanisms offered by random forest algorithms to reduce both the number of artificial outliers to generate and the size of the feature space in which they are generated. We show that One Class Random Forests perform well on various UCI public datasets in comparison to few other state-of-the-art one class classification methods (gaussian density models, Parzen estimators, gaussian mixture models and one-class SVMs).