Care of terminally ill / Palliative care

AI-Powered Radiotherapy for Resource-Limited Settings: Advancing Cervical and Prostate Cancer Treatment Planning with the Radiation Planning Assistant (RPA)

Radiotherapy treatment planning is a resource-intensive process characterized by multiple manual steps and clinical hand-offs that contribute to treatment delays and inter-observer variability. The Radiation Planning Assistant (RPA) is a web-based platform designed to deliver automated contouring and planning approaches tailored to low-resource settings. This work expands the RPA to develop and cl...

Lower pre-treatment TMS-evoked cortical reactivity and alpha-band oscillatory dynamics predict efficacy of primary motor cortex neuromodulation for chronic pain

Repetitive transcranial magnetic stimulation (rTMS) targeting the primary motor cortex (M1) provides significant pain relief in approximately 45% of patients with chronic pain. Identifying markers that predict rTMS treatment responders to M1 before initiating treatment is crucial for informing decision-making and improving patient outcomes in clinical practice. In this secondary analysis of a clin...

Interpretable machine learning and signal processing for automated reading and quality control of lateral flow tests for schistosomiasis

There is a lack of automated pipelines for diagnostic classification of point-of-care tests for neglected tropical diseases. Here we present an end-to...

Deep learning on 3D ECG geometry predicts ischemia

Three-dimensional (3D) electrocardiography (ECG) is a recent methodological advance that extends the dimensionality of the standard ECG, enabling geom...

A double-blind, crossover, non-inferiority randomized controlled trial where primary care providers and patients compare human- and AI-generated digital health messages: the AI-CARE study protocol

Primary care is facing multiple crises, including an increase in health misinformation. Digital health messaging by primary care providers has been sh...

A machine learning based authentication and intrusion detection scheme for IoT users anonymity preservation in fog environment.

Authentication is a critical challenge in fog computing security, especially as fog servers provide services to many IoT users. The conventional authe...

E2ED^2:Direct Mapping from Noise to Data for Enhanced Diffusion Models

Diffusion models have established themselves as the de facto primary paradigm in visual generative modeling, revolutionizing the field through remar...

ChartAdapter: Large Vision-Language Model for Chart Summarization

Chart summarization, which focuses on extracting key information from charts and interpreting it in natural language, is crucial for generating and ...

IMAGINE: An 8-to-1b 22nm FD-SOI Compute-In-Memory CNN Accelerator With an End-to-End Analog Charge-Based 0.15-8POPS/W Macro Featuring Distribution-Aware Data Reshaping

Charge-domain compute-in-memory (CIM) SRAMs have recently become an enticing compromise between computing efficiency and accuracy to process sub-8b ...

Transformer-Based Wireless Capsule Endoscopy Bleeding Tissue Detection and Classification

Informed by the success of the transformer model in various computer vision tasks, we design an end-to-end trainable model for the automatic detecti...

An End-to-End Depth-Based Pipeline for Selfie Image Rectification

Portraits or selfie images taken from a close distance typically suffer from perspective distortion. In this paper, we propose an end-to-end deep le...

Future Success Prediction in Open-Vocabulary Object Manipulation Tasks Based on End-Effector Trajectories

This study addresses a task designed to predict the future success or failure of open-vocabulary object manipulation. In this task, the model is req...

Efficiently Serving Large Multimodal Models Using EPD Disaggregation

Large Multimodal Models (LMMs) extend Large Language Models (LLMs) by handling diverse inputs such as images, audio, and video, but at the cost of a...

On the Feasibility of Vision-Language Models for Time-Series Classification

We build upon time-series classification by leveraging the capabilities of Vision Language Models (VLMs). We find that VLMs produce competitive resu...

Deep Joint Source Channel Coding for Privacy-Aware End-to-End Image Transmission

Deep neural network (DNN)-based joint source and channel coding is proposed for privacy-aware end-to-end image transmission against multiple eavesdr...

SilVar: Speech Driven Multimodal Model for Reasoning Visual Question Answering and Object Localization

Visual Language Models have demonstrated remarkable capabilities across tasks, including visual question answering and image captioning. However, mo...

Beyond End-to-End VLMs: Leveraging Intermediate Text Representations for Superior Flowchart Understanding

Flowcharts are typically presented as images, driving the trend of using vision-language models (VLMs) for end-to-end flowchart understanding. Howev...

SOUS VIDE: Cooking Visual Drone Navigation Policies in a Gaussian Splatting Vacuum

We propose a new simulator, training approach, and policy architecture, collectively called SOUS VIDE, for end-to-end visual drone navigation. Our t...

InstructSeg: Unifying Instructed Visual Segmentation with Multi-modal Large Language Models

Boosted by Multi-modal Large Language Models (MLLMs), text-guided universal segmentation models for the image and video domains have made rapid prog...

Experimental Study of Low-Latency Video Streaming in an ORAN Setup with Generative AI

Video streaming services depend on the underlying communication infrastructure and available network resources to offer ultra-low latency, high-qual...