Federico Bianchi is a postdoctoral researcher at Stanford University. His research, ranging from Natural Language Processing methods for textual analytics to recommender systems for e-commerce has been accepted to major NLP and AI conferences (EACL, NAACL, EMNLP, ACL, AAAI, RecSys) and journals (Cognitive Science, Applied Intelligence, Semantic Web Journal).
A visual-language foundation model for pathology image analysis using medical Twitter.
The lack of annotated publicly available medical images is a major barrier for computational research and education innovations. At the same time, many de-identified images and much knowledge are shared by clinicians on public forums such as medical Twitter. Here we harness these crowd platforms to curate OpenPath, a large dataset of 208,414 pathology images paired with natural language descriptions. We demonstrate the value of this resource by developing pathology language-image pretraining (PLIP), a multimodal artificial intelligence with both image and text understanding, which is trained on OpenPath. PLIP achieves state-of-the-art performances for classifying new pathology images across four external datasets: for zero-shot classification, PLIP achieves F1 scores of 0.565-0.832 compared to F1 scores of 0.030-0.481 for previous contrastive language-image pretrained model. Training a simple supervised classifier on top of PLIP embeddings also achieves 2.5% improvement in F1 scores compared to using other supervised model embeddings. Moreover, PLIP enables users to retrieve similar cases by either image or natural language search, greatly facilitating knowledge sharing. Our approach demonstrates that publicly shared medical information is a tremendous resource that can be harnessed to develop medical artificial intelligence for enhancing diagnosis, knowledge sharing and education.
View details for DOI 10.1038/s41591-023-02504-3
View details for PubMedID 37592105
View details for PubMedCentralID 9883475
- A challenge for rounded evaluation of recommender systems NATURE MACHINE INTELLIGENCE 2023
- Easily Accessible Text-to-Image Generation Amplifies Demographic Stereotypes at Large Scale ASSOC COMPUTING MACHINERY. 2023: 1493-1504
Viewpoint: Artificial Intelligence Accidents Waiting to Happen?
JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH
2023; 76: 193-199
View details for Web of Science ID 000915402300001
Contrastive language and vision learning of general fashion concepts.
2022; 12 (1): 18958
The steady rise of online shopping goes hand in hand with the development of increasingly complex ML and NLP models. While most use cases are cast as specialized supervised learning problems, we argue that practitioners would greatly benefit from general and transferable representations of products. In this work, we build on recent developments in contrastive learning to train FashionCLIP, a CLIP-like model adapted for the fashion industry. We demonstrate the effectiveness of the representations learned by FashionCLIP with extensive tests across a variety of tasks, datasets and generalization probes. We argue that adaptations of large pre-trained models such as CLIP offer new perspectives in terms of scalability and sustainability for certain types of players in the industry. Finally, we detail the costs and environmental impact of training, and release the model weights and code as open source contribution to the community.
View details for DOI 10.1038/s41598-022-23052-9
View details for PubMedID 36347888
"Does it come in black?" CLIP-like models are zero-shot recommenders
ASSOC COMPUTATIONAL LINGUISTICS-ACL. 2022: 191-198
View details for Web of Science ID 000846890000022