Zong Tuan Zhou

TL;DR: A 540-billion parameter, densely activated, Transformer language model, which is called PaLM achieves breakthrough performance, outperforming the state-of-the-art on a suite of multi-step reasoning tasks, and outperforming average human performance on the recently released BIG-bench benchmark.

TL;DR: This article proposed a CLIP-Driven Universal Model, which incorporates text embedding learned from Contrastive Language-Image Pre-training (CLIP) to segmentation models, enabling the model to learn a structured feature embedding and segment 25 organs and 6 types of tumors.

TL;DR: The results show that the pre-trained ViT performs comparably (sometimes better) to the state-of-the-art CNN (DenseNet-121) for multi-label thorax disease classification and remark that in-domain transfer learning is preferred whenever possible.

TL;DR: This paper seeks to address the cold start problem in vision active learning by exploiting the three advantages of contrastive learning: no annotation is required; (2) label diversity is ensured by pseudo-labels to mitigate bias; (3) typical data is determined by contrastive features to reduce outliers.

TL;DR: In this paper , the authors demonstrate that AI models can accurately segment liver tumors without the need for manual annotation by using synthetic tumors in CT scans, which are realistic in shape and texture, which even medical professionals can confuse with real tumors.