Researchers introduced LUX (Lesion-aware Unified eXplainable captioning), a graph-conditioned vision-language architecture for explainable endoscopic image captioning in ulcerative colitis, which cons…
Researchers proposed HiRA-CAM, an improved gradient-based visual explanation method for convolutional neural networks (CNNs), and showed it outperforms LayerCAM and Grad-CAM on creating saliency maps …
A new study from arXiv (2607.19382v1) finds that explainability methods such as attention rollout and Grad-CAM can reveal how continual learning models adapt to non-stationary time series, but also ex…
Explainable AI (XAI) is essential for deploying machine learning in high-stakes domains like healthcare, finance, and autonomous vehicles, where opaque models can lead to liability, non-compliance, or…
Nansense, a new interactive PyTorch debugger, allows developers to pause training, step batch-by-batch, and time-travel to different epochs while visualizing activations, gradients, weights, and optim…
AI is being deployed in production healthcare systems to read radiology scans, flag high-risk patients, and automate administrative tasks. Convolutional neural networks match radiologist performance o…