Osedea’s Post

"Exciting breakthrough in ultrasound segmentation! A new SimICL method combining visual in-context learning and masked image modeling achieved remarkable results, with a Dice coefficient of 0.96 and Jaccard Index of 0.92, surpassing state-of-the-art models. This could revolutionize AI assistance in ultrasound image analysis. #AI #MachineLearning #MedicalImaging #UltrasoundSegmentation"

Generative artificial intelligence: synthetic datasets in dentistry https://lnkd.in/gvTX6DjB Generative AI techniques can be useful in the production of Synthetic Datasets (SDs) that can overcome issues affecting traditionally acquired datasets. Variational autoencoders, generative adversarial networks and diffusion models have been used to generate SDs. The following text is a review of these generative AI techniques and their operations. It discusses the chances of SDs and challenges with potential solutions which will improve the understanding of healthcare professionals working in AI research.

Substack(#5) - Artificial Intelligence in Healthcare (Part 3): Medical Image Analysis and Applications - I A two-part article on the role of AI in medical image analysis. In this part, we will review the basics of image processing techniques and advanced neural networks. In the next, we will delve deeper into the applications of these neural networks. AI in Healthcare series: Part 1: Terminology and Fundamentals - https://lnkd.in/e4YctARw Part 2: Scope of Large Language Models - https://lnkd.in/eMb9-ucH Part 3: Medical Image Analysis and Applications - I - https://lnkd.in/eBa_TNaU #artificialintelligence #neuralnetworks #medicalimaging #healthcare #healthcareconsulting #healthtech

The Science: Worried That AI Might Make You Obsolete? In 2016, Geoffrey Hinton, a pioneer in deep learning, claimed, "We should stop training radiologists now, it's just completely obvious within five years deep learning is going to do better than radiologists". Similarly, in 2017, Vinod Khosla, a prominent venture capitalist, asserted that "the role of the radiologist will be obsolete in five years." Yet, these predictions from technology visionaries failed to materialize. What led to their glaring misjudgment? And, more broadly, what can we learn about AI-driven human obsolescence? https://buff.ly/3NbameF #ai #artificialintelligence #generativeai #airesearch #complexity #intimacyeconomy #chatgpt #complexchange #changemanagement #futureofwork #artificiality #mindforourminds #intimacyeconomy #agenticweb

The Science: Worried That AI Might Make You Obsolete? In 2016, Geoffrey Hinton, a pioneer in deep learning, claimed, "We should stop training radiologists now, it's just completely obvious within five years deep learning is going to do better than radiologists". Similarly, in 2017, Vinod Khosla, a prominent venture capitalist, asserted that "the role of the radiologist will be obsolete in five years." Yet, these predictions from technology visionaries failed to materialize. What led to their glaring misjudgment? And, more broadly, what can we learn about AI-driven human obsolescence? https://buff.ly/3NbameF #ai #artificialintelligence #generativeai #airesearch #complexity #intimacyeconomy #chatgpt #complexchange #changemanagement #futureofwork #artificiality #mindforourminds #intimacyeconomy #agenticweb

📓 This paper presents a comprehensive overview of convolutional neural network (CNN) methods for medical image classification, highlighting their potential to revolutionize the analysis of medical images. Through a meticulous survey, the paper delves into various aspects of CNN design, optimization, and application, while also employing statistical modeling to identify trends and gaps in current research. #medicalimaging; #convolutionalneuralnetworkmodels; #classification; #hyperparametertuning; #frameworks; #preprocessing; #performancemetrics; #ensemble; #activationfunction "Medical Image Classifications Using Convolutional Neural Networks: A Survey of Current Methods and Statistical Modeling of the Literature" By Foziya Ahmed, Kula Tune, Beakal Gizachew Assefa, Marti Jett and Seid Muhie 🔗 https://lnkd.in/g8uDUfyM

AI Artificial intelligence algorithms used for dental caries detection AI, particularly machine learning and deep learning algorithms, has shown promising results in detecting and diagnosing dental caries. These algorithms use vast amounts of dental image data to learn the patterns and characteristics associated with caries, enabling the viewing of the condition in new, unseen images accurately [15]. Predictive modeling leverages data and statistical algorithms to forecast outcomes with optimal accuracy. The incorporation of AI, especially machine learning algorithms, has greatly enhanced the strength and precision of predictive modeling. A systematic review deduced that deep learning models, employing a wide array of architectures, can prove instrumental in aiding dentists in the detection of caries lesions  https://lnkd.in/eE2VXZDP

Rapid advances have occurred at the intersection of AI and medicine over the last few years, especially in the interpretation of medical images. In this talk, Pranav Rajpurkar, PhD, describes three key directions that present challenges and opportunities for developing deep learning technologies for medical image interpretation.

✍️List of widely used some machine learning algorithms for medical image processing 🎯Medical image processing leverages various #machinelearning algorithms to analyze and interpret medical images for tasks such as diagnosis, #segmentation, classification, and anomaly detection. 🎯 Here are some widely used machine learning #algorithms in this domain: 1. Convolutional Neural Networks (CNNs) Use Cases: Image classification, segmentation, & detection. Examples: ResNet, VGG, Inception, U-Net. Why: #CNNs are excellent at capturing spatial hierarchies and patterns in images, making them ideal for medical imaging tasks. 2. Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs) Use Cases: Sequence #prediction, time-series analysis in medical imaging (e.g., MRI slices over time). Examples: Standard RNNs, LSTMs. Why: These networks can handle sequential data and are useful for tasks where the #temporal sequence of images is important. 3. Generative Adversarial Networks (GANs) Use Cases: Image generation, enhancement, & augmentation. Examples: Vanilla GAN, Conditional GAN, CycleGAN. Why: GANs can generate high-quality synthetic medical images & #enhance the quality of existing images, aiding in better diagnosis and training data augmentation. 4. Autoencoders Use Cases: Image denoising, compression, & anomaly detection. Examples: Standard #autoencoders, Variational Autoencoders (VAEs). Why: Autoencoders can learn efficient representations of medical images, which are useful for tasks like #noise reduction & feature extraction. 5. Support Vector Machines (SVMs) Use Cases: Classification tasks in medical imaging. Why: SVMs to be effective in high-dimensional spaces & can be used for tasks like #tumor classification in medical images. 6. Random Forests Use Cases: Image classification & feature selection. Why: Random forests are robust to overfitting and can handle large datasets with many #features, making them suitable for certain classification tasks in medical #imaging. 7. k-Nearest Neighbors (k-NN) Use Cases: Image classification. Why: k-NN is a simple yet effective algorithm for image classification tasks, although it can be computationally expensive with large #datasets. 8. Principal Component Analysis (PCA) and Independent Component Analysis (ICA) Use Cases: Dimensionality reduction & feature extraction. Why: These techniques are useful for reducing the #dimensionality of medical image data, making it easier to analyze and visualize. 🎯These algorithms, particularly when tailored & combined, offer #powerful tools for analyzing medical images, assisting in diagnostic accuracy, #treatment planning, & medical research. 🎯The choice of algorithm depends on the specific task, the nature of the #medical images, & the available computational resources. #phdresearch #researchskills #researchguidance #AI #researcharticle

#NLP methods can help physicians to automatically identify and extract relevant information by analyzing #radiology text reports, collect large radiology image datasets as well as automate image collection and annotation. Via https://lnkd.in/ghyvJMXZ #naturallanguageprocessing #medicalimaging #deeplearning #imageanalysis #aiforhealthcare