Automated Histopathologic Assessment of Hirschsprung Disease Using a Multi-Stage Vision Transformer Framework

The introduction of DinoLizer, a model based on DINOv2, aims to enhance the localization of manipulated regions in generative inpainting. By utilizing a pretrained DINOv2 model on the B-Free dataset, it incorporates a linear classification head to predict manipulations at a granular patch resolution, employing a sliding-window strategy for larger images. This method shows superior performance compared to existing local manipulation detectors across various datasets.

LLaVA-UHD v3 has been introduced as a new multi-modal large language model (MLLM) that utilizes Progressive Visual Compression (PVC) for efficient native-resolution encoding, enhancing visual understanding capabilities while addressing computational overhead. This model integrates refined patch embedding and windowed token compression to optimize performance in vision-language tasks.

A new model named SMARC has been introduced, enabling surface material reconstruction and classification from minimal visual cues, specifically using just a 10% contiguous patch of an image. This approach addresses the limitations of existing methods that require dense observations, making it particularly useful in constrained environments.

A recent study has introduced a modular, on-site solution for sustainable nutrient management in agriculture, utilizing lightweight anomaly detection techniques to optimize nutrient consumption and enhance crop growth. The approach employs a tiered pipeline for status estimation and anomaly detection, integrating multispectral imaging and an autoencoder for early warnings during nutrient depletion experiments.

A new method for classifying glioblastoma subregions using a contrastive learning-based encoder has been developed, achieving notable performance metrics in the BraTS-Path 2025 Challenge. The model, which fine-tunes a pre-trained Vision Transformer, secured second place with an MCC of 0.6509 and an F1-score of 0.5330 on the final test set.

CAMformer has been introduced as a novel accelerator that reinterprets attention mechanisms in Transformers as associative memory operations, utilizing a Binary Attention Content Addressable Memory (BA-CAM) to enhance energy efficiency and throughput while maintaining accuracy. This innovation addresses the scalability challenges faced by traditional Transformers due to the quadratic cost of attention computations.

The introduction of EVCC (Enhanced Vision Transformer-ConvNeXt-CoAtNet) marks a significant advancement in hybrid vision architectures, integrating Vision Transformers, lightweight ConvNeXt, and CoAtNet. This multi-branch architecture employs innovative techniques such as adaptive token pruning and gated bidirectional cross-attention, achieving state-of-the-art accuracy on various datasets while reducing computational costs by 25 to 35% compared to existing models.

A recent study has demonstrated that large-scale pre-training using self-supervised learning can effectively differentiate radiation necrosis from tumor progression in brain metastases using routine MRI scans. This approach utilized a Vision Transformer model pre-trained on over 10,000 unlabeled MRI sub-volumes and fine-tuned on a public dataset, achieving promising results in classification accuracy.