Interpretable Retinal Disease Prediction Using Biology-Informed Heterogeneous Graph Representations

The article discusses a new framework for knowledge distillation (KD) that focuses on feature-based losses rather than traditional logit-based losses. This approach aims to enhance the training of lightweight student models by utilizing effective knowledge from teacher models. The proposed method demonstrates superior performance across various image classification datasets, achieving state-of-the-art results.

Foundation models (FMs) are revolutionizing medical image analysis by leveraging large datasets of unlabeled data. Unlike traditional methods that depend on manually annotated examples, FMs are pre-trained to extract general visual features, which can be fine-tuned for specific clinical tasks with minimal supervision. This review explores the development and application of FMs in pathology, radiology, and ophthalmology, synthesizing insights from over 150 studies. It highlights the components of FM pipelines and discusses challenges and future research directions.

ReLaX-Net proposes a novel approach to enhance the efficiency of Physical Neural Networks (PNNs) by reusing layers. PNNs are seen as promising for future computing systems, yet they currently lag behind digital neural networks in terms of scale and performance. This research focuses on hardware-friendly weight-tying methods, addressing the challenge of slow training elements in PNNs compared to their fast dynamic components. The study aims to improve the parameter efficiency of PNNs, drawing parallels with early advancements in digital neural networks.

Flood prediction is essential for emergency planning and response to reduce human and economic losses. Traditional hydrodynamic models create high-resolution flood maps but are computationally intensive and impractical for real-time applications. Recent studies using convolutional neural networks for flood map super-resolution have shown good accuracy but lack generalizability. This paper introduces a novel approach using latent diffusion models to enhance coarse-grid flood maps, achieving fine-grid accuracy while significantly reducing inference time.

Recent research highlights the potential of task arithmetic for editing pre-trained closed-vocabulary models, particularly in image classification. This study investigates task addition in closed-vocabulary models, revealing that weight disentanglement is a common outcome of pre-training. The findings suggest that closed-vocabulary vision transformers can be effectively modified using task arithmetic, leading to enhanced multi-task model deployment capabilities.

A new clustering-based explainability technique for digital pathology models using convolutional neural networks has been introduced. This method differs from traditional saliency map techniques by providing a global view of model behavior while offering detailed insights. The technique enhances understanding and confidence in model predictions, potentially accelerating clinical adoption. Its effectiveness was evaluated on a prostate cancer detection model, showcasing its practical utility in medical diagnostics.

A novel approach to enhancing seismic foundation models has been introduced, synergizing multigrid algorithms with vision transformers. This method addresses the unique characteristics of seismic data, which require specialized processing techniques. The proposed adaptive two-grid foundation model training strategy (ADATG) utilizes Hilbert encoding to effectively capture both high- and low-frequency features in seismogram data, improving the efficiency of seismic data analysis and model training.

This article provides an in-depth exploration of object detection and semantic segmentation, merging theoretical foundations with practical applications. It reviews advancements in machine learning and deep learning, particularly focusing on convolutional neural networks (CNNs), YOLO architectures, and transformer-based approaches like DETR. The study also examines the integration of AI techniques and large language models to enhance object detection in complex environments, along with a comprehensive analysis of big data processing and model optimization.