548 skills found · Page 8 of 19
marccasian / KaryML FrameworkMachine Learning (ML) research within medicine and healthcare represents one of the most challenging domains for both engineers and medical specialists. One of the most desired tasks to be accomplished using ML applications is represented by disease detection. A good example of such a task is the detection of genetic abnormalities like Down syndrome, Klinefelter syndrome or Hemophilia. Usually, clinicians are doing chromosome analysis using the karyotype to detect such disorders. The main contribution of the current article consists of introducing a new approach called KaryML Framework, which is extending our previous research: KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps . Our major goal is to provide a new method for an automated karyotyping system using unsupervised techniques. Additionally, we provide computational methods for chromosome feature extraction and to develop an intelligent system designed to aid clinicians during the karyotyping process.
JayJhaveri1906 / CSE291 MedLMAutomated systems are crucial for summarizing medical information. Large Language Models (LLMs) show promise in healthcare, specifically for Closed-Book Generative QnA. This study compares general and medical-specific LMs, evaluates their performance in medical Q&A, and provides insights into their suitability for medical applications.
KalyanM45 / Medical AssisstantThe Medical Query Generator is a web application that leverages Google's GenerativeAI to generate detailed and accurate medical responses. Users input medical queries, and the application provides responses adhering to specific guidelines for clarity, accuracy, and informativeness.
goyaljai / Healint AppHealint is a way to assist doctors and patients in disease diagnosis and their treatment. It has a doctor bot which interacts with patients and finally predict the disease. Other than this it also has location based hospital recommendation system. It also facilitates in knowing your BMI and getting a diet chart
MaksymPetyak / MedplexityEvaluating LLMs for medical applications
PRLab-FAU / Mt Lecture SlidesThese are the lecture slides used at FAU Erlangen-Nuremberg, Germany for the lecture "Medical Engineering". This class gives a complete and comprehensive introduction to the fields of medical imaging systems, as designed for a broad range of applications. The authors of the book first explain the foundations of system theory and image processing, before highlighting several modalities in a dedicated chapter. The initial focus is on modalities that are closely related to traditional camera systems such as endoscopy and microscopy. This is followed by more complex image formation processes: magnetic resonance imaging, X-ray projection imaging, computed tomography, X-ray phase-contrast imaging, nuclear imaging, ultrasound, and optical coherence tomography. Open Access Link to the Text Book: https://link.springer.com/book/10.1007/978-3-319-96520-8#about Link to Video Recordings on YouTube: https://www.youtube.com/watch?v=vvftvjnXzsY&list=PLpOGQvPCDQzsgK1XuhUXO8r9M4WRqhvDf
donglongzi / UNETR PytorchThis repository contains the code for UNETR: Transformers for 3D Medical Image Segmentation [1]. UNETR is the first 3D segmentation network that uses a pure vision transformer as its encoder without relying on CNNs for feature extraction. The code presents a volumetric (3D) multi-organ segmentation application using the BTCV challenge dataset.
sushruthreddygade / ItuHMSPurpose The purpose of this project is to apply and learn advanced software engineering concepts gathering requirements for a software application that schedules the hospital personnel and then derive use cases from them. This involves reviewing of already existing software and learning website and derive requirements and use cases based on the website primary features. The project also encompasses construction of, sequence diagram, design class diagram, Collaboration diagrams and other UML modeling diagrams based on the derived use cases. At the end, high level planning is done for the whole project based on derived use cases by Agile efforts estimation technique. Scope The project will consist of developing personnel scheduling software. Modules of the website include a login feature, a schedule checker and a schedule planner. Our innovative 100% web-based Scheduling & Open Shift Management (OSM) product can help managers efficiently schedule their staff, and also lets the staff help managers fill open shifts online, see schedules and changes, request time- off, swap shifts, etc, etc. All schedules, changes, approvals, and alerts not only happen online in real-time, but also are sent out as emails and text messages to cell phones. Our Software can cut manager time wasted on scheduling tasks by 50% or more and let them get back to MANAGING! Introduction to HMSS When workforce includes hundreds of employees, open shifts are inevitable. Without the right skills-based workforce management tools in place, nurse managers and staffing managers spend a disproportionate amount of time trying to fill scheduling gaps. Not only is this inefficient, but it leads to increased costs and reduced employee satisfaction. Advantages of having online scheduling system 2.1. ● Save Money Reduce premium labor costs by leveraging the most cost-effective, qualified staff to fill open shifts. Stop wasting time you don’t have on scheduling. Decrease the time it takes you to create a weekly schedule for your team by over 75 percent with HMSS. ● Save Time Save countless hours using instant communication strategies to fill open shifts. ● Keep Workers Happy Increase employee satisfaction by empowering them to choose when they want to work based on experience, competencies, and skills. ● Faster, Easier Scheduling Healthcare staff scheduling has never been easier. Spend minutes instead of hours organizing shifts for your nurses and other medical staff. ● Monitor Attendance Hospital Management System (Personal Scheduling System) – SWE 600 (Fall 2015) Prof. Instructors: Dr. R. Riehle & Q Asghar See which employees are coming in late or missing shifts. Send shift reminders automatically to make sure everyone is on the same page. ● Give Staff More Independence Take some work off your own plate with collaborative healthcare staff scheduling options. Allow your employees to request shift trades and swaps on their own so you don’t have to micromanage them. ● Avoid Human Error Humans make mistakes, but HMSS doesn’t. If you forget to fill a shift or overbook one, the software immediately notifies you of your error, allowing you to rectify it right away. ● Create Perfect Timesheets Export perfect timesheets to create perfect attendance and work reports. Better healthcare staff scheduling means easier payroll processing as well. To deliver the best care possible to patients, we must begin with the best possible workforce management solution for our staff. With HMSS, we help control labor costs, minimize compliance risk, improve workforce productivity, and deliver quality, cost-effective care. Here’s how: • Physician coverage scheduling enables the effective and equitable deployment of physicians and other clinician providers – your group, your rules, to build your schedules • Advanced staffing supports the safest and most appropriate assignment of caregiver staff to patients, and balances workload distribution in the best interests of patients and staff • Intelligently forecast volume to build optimal schedules helps ensure proper staff coverage for every shift, every day, across your entire organization • On-demand visibility with labor analytics controls labor costs and allows organizations to make evidence-based decisions • Mobile management supports paperless workflow and employee self-service for your on-the-go workforce
vinayakumarr / ICMRICMR Sponsored Seminar On Deep Learning Techniques and Tools for Medical Applications
deepgram-devs / Voice Agent Medical Assistant DemoA Medical / Clinical Note Taking Demo Application using Deepgram Voice Agent API
Atul-Anand-Jha / Pharmacy Management SoftwareMedical Store Management Software written in C#; with Ui/UX (Windows Form Application)
stevend12 / SolutioCppA C++ library for applications of physics in medicine, particularly radiation therapy and medical imaging
ericrisco / Medical Gemma 3nA comprehensive pipeline for training and deploying medical AI models for offline emergency assistance. This project fine-tunes Gemma-3n models on medical datasets and deploys them in a Flutter mobile application for edge computing scenarios.
mohamedmagdy2301 / CurAi App MobileCurAi is an innovative Smart Medical Appointment Booking System designed to simplify and enhance the process of scheduling medical appointments. This mobile application, built with Flutter, offers a seamless experience for users to search for doctors, book appointments, and receive personalized recommendations through an AI-powered chatbot.
oresths / Niftyreg BsiThe accompanying code of "Accelerating B-spline Interpolation on GPUs: Application to Medical Image Registration"
nicholasmcconnell / MyEHRThis full-stack web application follows the MVC design pattern for users to input and save their personal medical health records in a database (MongoDB).
KatherLab / MediSwarmMediSwarm is an open-source project for advancing medical deep learning using swarm intelligence and NVFlare. Developed by the Odelia consortium, it ensures data privacy through federated learning, enabling collaborative, decentralized model training across institutions for improved medical research and applications.
MainakVerse / Brain Scan AIThis application uses deep learning to analyze brain MRI images and classify them into different categories of brain tumors. The system is designed to assist medical professionals in the diagnostic process.
Zainul-Code / Medich AppMedich is an application that raises the problem of satisfaction related to BPJS, especially in the lower classes, with Medich being able to solve all these problems. Medich has a Donation feature that will be very useful for the Community
nandhini-1402 / MULTIPLE DISEASEComprehensive dataset repository facilitating multiple disease prediction research. Curated datasets cover various medical conditions, enabling robust analysis and model development for predictive healthcare applications. A valuable resource for advancing disease diagnosis and treatment strategies
