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Learning Treatment Policies From Multimodal Electronic Health Recordscs.AI updates on arXiv.org arXiv:2507.20993v2 Announce Type: replace-cross
Abstract: We study how to learn effective treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators assume tabular covariates that satisfy strong causal assumptions, which are typically violated in the multimodal setting. As a result, predictive models of baseline risk are commonly used in practice to guide such decisions, as they extend naturally to multimodal data. However, such risk-based policies are not designed to identify which patients benefit most from treatment. We propose an extension of causal policy learning that uses expert-provided annotations during training to supervise treatment effect estimation, while using only multimodal representations as input during inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, thereby offering practical insights into applying causal machine learning to realistic clinical data.

 arXiv:2507.20993v2 Announce Type: replace-cross
Abstract: We study how to learn effective treatment policies from multimodal electronic health records (EHRs) that consist of tabular data and clinical text. These policies can help physicians make better treatment decisions and allocate healthcare resources more efficiently. Causal policy learning methods prioritize patients with the largest expected treatment benefit. Yet, existing estimators assume tabular covariates that satisfy strong causal assumptions, which are typically violated in the multimodal setting. As a result, predictive models of baseline risk are commonly used in practice to guide such decisions, as they extend naturally to multimodal data. However, such risk-based policies are not designed to identify which patients benefit most from treatment. We propose an extension of causal policy learning that uses expert-provided annotations during training to supervise treatment effect estimation, while using only multimodal representations as input during inference. We show that the proposed method achieves strong empirical performance across synthetic, semi-synthetic, and real-world EHR datasets, thereby offering practical insights into applying causal machine learning to realistic clinical data. Read More  

Zero-Shot Segmentation through Prototype-Guidance for Multi-Label Plant Species Identificationcs.AI updates on arXiv.org arXiv:2512.19957v1 Announce Type: new
Abstract: This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set images. To obtain these representations, the proposed method extracts features from training dataset images and create clusters, by applying K-Means, with $K$ equals to the number of classes in the dataset. The segmentation model is a customized narrow ViT, built by replacing the patch embedding layer with a frozen DinoV2, pre-trained on the training dataset for individual species classification. This model is trained to reconstruct the class prototypes of the training dataset from the test dataset images. We then use this model to obtain attention scores that enable to identify and localize areas of interest and consequently guide the classification process. The proposed approach enabled a domain-adaptation from multi-class identification with individual species, into multi-label classification from high-resolution vegetation plots. Our method achieved fifth place in the PlantCLEF 2025 challenge on the private leaderboard, with an F1 score of 0.33331. Besides that, in absolute terms our method scored 0.03 lower than the top-performing submission, suggesting that it may achieved competitive performance in the benchmark task. Our code is available at href{https://github.com/ADAM-UEFS/PlantCLEF2025}{https://github.com/ADAM-UEFS/PlantCLEF2025}.

 arXiv:2512.19957v1 Announce Type: new
Abstract: This paper presents an approach developed to address the PlantClef 2025 challenge, which consists of a fine-grained multi-label species identification, over high-resolution images. Our solution focused on employing class prototypes obtained from the training dataset as a proxy guidance for training a segmentation Vision Transformer (ViT) on the test set images. To obtain these representations, the proposed method extracts features from training dataset images and create clusters, by applying K-Means, with $K$ equals to the number of classes in the dataset. The segmentation model is a customized narrow ViT, built by replacing the patch embedding layer with a frozen DinoV2, pre-trained on the training dataset for individual species classification. This model is trained to reconstruct the class prototypes of the training dataset from the test dataset images. We then use this model to obtain attention scores that enable to identify and localize areas of interest and consequently guide the classification process. The proposed approach enabled a domain-adaptation from multi-class identification with individual species, into multi-label classification from high-resolution vegetation plots. Our method achieved fifth place in the PlantCLEF 2025 challenge on the private leaderboard, with an F1 score of 0.33331. Besides that, in absolute terms our method scored 0.03 lower than the top-performing submission, suggesting that it may achieved competitive performance in the benchmark task. Our code is available at href{https://github.com/ADAM-UEFS/PlantCLEF2025}{https://github.com/ADAM-UEFS/PlantCLEF2025}. Read More  

FGDCC: Fine-Grained Deep Cluster Categorization — A Framework for Intra-Class Variability Problems in Plant Classificationcs.AI updates on arXiv.org arXiv:2512.19960v1 Announce Type: new
Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained Visual Categorization (FGVC) tasks. This paper proposes a novel method that aims at leveraging classification performance in FGVC tasks by learning fine-grained features via classification of class-wise cluster assignments. Our goal is to apply clustering over each class individually, which can allow to discover pseudo-labels that encodes a latent degree of similarity between images. In turn, those labels can be employed in a hierarchical classification process that allows to learn more fine-grained visual features and thereby mitigating intra-class variability issues. Initial experiments over the PlantNet300k enabled to shed light upon several key points in which future work will have to be developed in order to find more conclusive evidence regarding the effectiveness of our method. Our method still achieves state-of-the-art performance on the PlantNet300k dataset even though some of its components haven’t been shown to be fully optimized. Our code is available at href{https://github.com/ADAM-UEFS/FGDCC}{https://github.com/ADAM-UEFS/FGDCC}.

 arXiv:2512.19960v1 Announce Type: new
Abstract: Intra-class variability is given according to the significance in the degree of dissimilarity between images within a class. In that sense, depending on its intensity, intra-class variability can hinder the learning process for DL models, specially when such classes are also underrepresented, which is a very common scenario in Fine-Grained Visual Categorization (FGVC) tasks. This paper proposes a novel method that aims at leveraging classification performance in FGVC tasks by learning fine-grained features via classification of class-wise cluster assignments. Our goal is to apply clustering over each class individually, which can allow to discover pseudo-labels that encodes a latent degree of similarity between images. In turn, those labels can be employed in a hierarchical classification process that allows to learn more fine-grained visual features and thereby mitigating intra-class variability issues. Initial experiments over the PlantNet300k enabled to shed light upon several key points in which future work will have to be developed in order to find more conclusive evidence regarding the effectiveness of our method. Our method still achieves state-of-the-art performance on the PlantNet300k dataset even though some of its components haven’t been shown to be fully optimized. Our code is available at href{https://github.com/ADAM-UEFS/FGDCC}{https://github.com/ADAM-UEFS/FGDCC}. Read More  

Is Your Model Time-Blind? The Case for Cyclical Feature EncodingTowards Data Science How cyclical encoding improves machine learning prediction
The post Is Your Model Time-Blind? The Case for Cyclical Feature Encoding appeared first on Towards Data Science.

 How cyclical encoding improves machine learning prediction
The post Is Your Model Time-Blind? The Case for Cyclical Feature Encoding appeared first on Towards Data Science. Read More