Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 56,931 to 56,940 of 227,153 articles

Just Noticeable Difference Modeling for Deep Visual Features

arXiv
Deep visual features are increasingly used as the interface in vision systems, motivating the need to describe feature characteristics and control feature quality for machine perception. Just noticeable difference (JND) characterizes the maximum impe... read more 

BookNet: Book Image Rectification via Cross-Page Attention Network

arXiv
Book image rectification presents unique challenges in document image processing due to complex geometric distortions from binding constraints, where left and right pages exhibit distinctly asymmetric curvature patterns. However, existing single-page... read more 

Robust Multimodal Representation Learning in Healthcare

arXiv
Medical multimodal representation learning aims to integrate heterogeneous data into unified patient representations to support clinical outcome prediction. However, real-world medical datasets commonly contain systematic biases from multiple sources... read more 

Embracing Aleatoric Uncertainty in Medical Multimodal Learning with Missing Modalities

arXiv
Medical multimodal learning faces significant challenges with missing modalities prevalent in clinical practice. Existing approaches assume equal contribution of modality and random missing patterns, neglecting inherent uncertainty in medical data ac... read more 

PaddleOCR-VL-1.5: Towards a Multi-Task 0.9B VLM for Robust In-the-Wild Document Parsing

arXiv
We introduce PaddleOCR-VL-1.5, an upgraded model achieving a new state-of-the-art (SOTA) accuracy of 94.5% on OmniDocBench v1.5. To rigorously evaluate robustness against real-world physical distortions, including scanning, skew, warping, screen-phot... read more 

MoE-ACT: Improving Surgical Imitation Learning Policies through Supervised Mixture-of-Experts

arXiv
Imitation learning has achieved remarkable success in robotic manipulation, yet its application to surgical robotics remains challenging due to data scarcity, constrained workspaces, and the need for an exceptional level of safety and predictability.... read more 

Investigation into using stochastic embedding representations for evaluating the trustworthiness of the Fréchet Inception Distance

arXiv
Feature embeddings acquired from pretrained models are widely used in medical applications of deep learning to assess the characteristics of datasets; e.g. to determine the quality of synthetic, generated medical images. The Fréchet Inception Distanc... read more 

Investigating Batch Inference in a Sequential Monte Carlo Framework for Neural Networks

arXiv
Bayesian inference allows us to define a posterior distribution over the weights of a generic neural network (NN). Exact posteriors are usually intractable, in which case approximations can be employed. One such approximation - variational inference ... read more 

Visual-Guided Key-Token Regularization for Multimodal Large Language Model Unlearning

arXiv
Unlearning in Multimodal Large Language Models (MLLMs) prevents the model from revealing private information when queried about target images. Existing MLLM unlearning methods largely adopt approaches developed for LLMs. They treat all answer tokens ... read more 

Hybrid Foveated Path Tracing with Peripheral Gaussians for Immersive Anatomy

arXiv
Volumetric medical imaging offers great potential for understanding complex pathologies. Yet, traditional 2D slices provide little support for interpreting spatial relationships, forcing users to mentally reconstruct anatomy into three dimensions. Di... read more