# OASIS Published articles for OASIS. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## AdaptAD: Foundation Model Adaptation with Missing Modality Robustness for Alzheimer's Disease Staging Across the Disease Continuum DevFeed: [AdaptAD: Foundation Model Adaptation with Missing Modality Robustness for Alzheimer's Disease Staging Across the Disease Continuum]() Original publisher: [Read original article]() Author: Dawa Chyophel Lepcha, Sophie A. Martin, Pierrick Coup\\'e, Shabbir Syed Abdul Published: 2026-11-03T05:00:00Z Content type: article Language: en Sources: [arXiv: Machine Learning]() Topics: [AI Foundation Models](), [lora](), [Fine-tuning](), [ImageNet]() Tags: [3-d](), [cdr](), [cs-cv](), [cs-lg](), [disease](), [fine-tuning](), [foundation-model](), [lora](), [model](), [oasis](), [robustness](), [staging]() ### AI overview AdaptAD stages cognitively normal, MCI, and Alzheimer's dementia from registered T1 MRI using 16 medial-temporal 2.5-D slices and a frozen ImageNet ViT-B/16 adapted with LoRA; morphometry is optional, with a learned missing token and a stacker to fuse available inputs. In five-fold ADNI evaluation (597 participants, 29% lacking FreeSurfer), it achieved macro-AUC 0.745 and significantly exceeded full fine-tuning, ShaSpec, and a 3-D CNN. On a locked OASIS-3 evaluation, macro-AUC was 0.780; the CDR-based label score was 0.804 and significantly surpassed the compared 3-D and image-tabular methods. LoRA matched full fine-tuning with 25-fold fewer trainable parameters, and omitting morphometry changed external macro-AUC by less than 0.003. The reported disease score tracked cognitive measures and stratified MCI-to-dementia progression; these findings are from the described cohorts and protocol. ### Source excerpt arXiv:2611.00084v1 Announce Type: cross Abstract: Staging Alzheimer's disease (AD) across its continuum, from cognitively normal (CN) through mild cognitive impairment (MCI) to dementia from a single structural MRI remains difficult because 3-D networks overfit small cohorts, and FreeSurfer morphometry is often unavailable. We present AdaptAD, which reads a registered T1-weighted scan as 16 anatomically placed 2.5-D slices through the medial temporal lobe and adapts a frozen ImageNet-pretrained ViT-B/16 with low-rank adaptation (LoRA), training 3.5 M of 89 M parameters. Morphometry enters as tokens when present, or as a learned [MISSING] token, and a missing-aware stacker fuses both experts. Under a pre-registered protocol, ten methods were compared for three-class CN/MCI/AD staging on identical 5-fold splits of 597 ADNI participants, 29% without FreeSurfer, with Holm-corrected paired bootstrap tests. The locked model was tested once on 993 OASIS-3 participants (988 scored by every method). On ADNI, AdaptAD achieved a three-class macro-AUC of 0.745 (95% CI 0.709 to 0.781), ranking first and significantly exceeding full fine-tuning, ShaSpec and a 3-D CNN. On OASIS-3 it reached 0.780 (0.748 to 0.814), and on the CDR-based label (0.804) it significantly outperformed every 3-D and image-tabular method. LoRA matched full fine-tuning with 25 times fewer trainable parameters, and removing morphometry changed external macro-AUC by less than 0.003. The disease score tracked MMSE and CDR-SB within diagnostic groups and stratified MCI-to-dementia progression (log-rank p < 0.001), while saliency concentrated in medial temporal and periventricular regions. A parameter-efficient 2-D foundation model can therefore stage the full AD continuum from a routine T1 scan without morphometry, offering a scalable route to memory-clinic deployment. ## Report: Amazon in talks with AI startup Decart over possible $7B acquisition DevFeed: [Report: Amazon in talks with AI startup Decart over possible $7B acquisition]() Original publisher: [Read original article]() Author: Mike Wheatley Published: 2026-10-12T00:31:19Z Content type: news Language: en Sources: [SiliconANGLE]() Topics: [AI Infrastructure](), [Artificial Intelligence](), [AWS Trainium](), [Amazon SageMaker JumpStart]() Tags: [acquisition](), [acquisition-talks](), [ai](), [ai-processors](), [ai-startup](), [ai-training-and-inference](), [ai-video](), [amazon](), [amazon-web-services](), [anthropic](), [applications](), [arm](), [availability](), [aws-chips](), [benchmark](), [bloomberg](), [cloud](), [co-founder](), [company](), [customer-support](), [decart-ai](), [developer](), [lucy](), [neural-networks](), [news](), [oasis](), [software](), [startup](), [the-latest](), [top-story-5](), [trainium3](), [world-models]() ### AI overview Amazon is reportedly discussing a possible acquisition of Decart, an AI startup whose DOS software helps developers optimize neural networks for different processors. The article says this can shorten optimization work from months to weeks and help teams move workloads when chip availability or requirements change. The deal could be worth up to $7 billion, though the discussions may not result in an acquisition. ### Source excerpt Amazon.com Inc. is reportedly engaged in discussion over the potential acquisition of artificial intelligence developer Decart.ai Inc. in a deal that could be worth up to $7 billion. Decart has developed software that makes it easier for developers to run their AI models on different processors, which has proven to be a valuable capability as [...] The post Report: Amazon in talks with AI startup Decart over possible $7B acquisition appeared first on SiliconANGLE.