This article was originally featured on The Conversation. Languages are windows into the worlds of the people who speak them – reflecting what they value and experience daily. So perhaps it's no surprise different languages highlight different areas of vocabulary. Scholars have noted that Mongolian has many horse-related words, that Maori has many words for ferns, and Japanese has many words related to taste. Some links are unsurprising, such as German having many words related to beer, or Fijian having many words for fish.

In the context of medical imaging and machine learning, one of the most pressing challenges is the effective adaptation of pre-trained models to specialized medical contexts. Despite the availability of advanced pre-trained models, their direct application to the highly specialized and diverse field of medical imaging often falls short due to the unique characteristics of medical data. This study provides a comprehensive analysis on the performance of various fine-tuning methods applied to pre-trained models across a spectrum of medical imaging domains, including X-ray, MRI, Histology, Dermoscopy, and Endoscopic surgery. We evaluated eight fine-tuning strategies, including standard techniques such as fine-tuning all layers or fine-tuning only the classifier layers, alongside methods such as gradually unfreezing layers, regularization based fine-tuning and adaptive learning rates. We selected three well-established CNN architectures (ResNet-50, DenseNet-121, and VGG-19) to cover a range of learning and feature extraction scenarios. Although our results indicate that the efficacy of these fine-tuning methods significantly varies depending on both the architecture and the medical imaging type, strategies such as combining Linear Probing with Full Fine-tuning resulted in notable improvements in over 50% of the evaluated cases, demonstrating general effectiveness across medical domains. Moreover, Auto-RGN, which dynamically adjusts learning rates, led to performance enhancements of up to 11% for specific modalities. Additionally, the DenseNet architecture showed more pronounced benefits from alternative fine-tuning approaches compared to traditional full fine-tuning. This work not only provides valuable insights for optimizing pre-trained models in medical image analysis but also suggests the potential for future research into more advanced architectures and fine-tuning methods.

Humans express ideas, beliefs, and statements through language. The manner of expression can carry information indicating the author's degree of confidence in their statement. Understanding the certainty level of a claim is crucial in areas such as medicine, finance, engineering, and many others where errors can lead to disastrous results. In this work, we apply a joint model that leverages words and part-of-speech tags to improve hedge detection in text and achieve a new top score on the CoNLL-2010 Wikipedia corpus.

For lack of rich competition, some of Nvidia's most significant results in the latest MLPerf were against itself, comparing its newest GPU, H100 "Hopper," to its existing product, the A100. Although chip giant Nvidia tends to cast a long shadow over the world of artificial intelligence, its ability to simply drive competition out of the market may be increasing, if the latest benchmark test results are any indication. Did you miss out on Black Friday 2022? No problem: Cyber Monday deals are here, with internet retailers offering their lowest prices of the year. ZDNET is surfacing the latest and best sales online in real time for you to check out now.