6 papers · 1 filter
Locality-Attending Vision Transformer
Sina Hajimiri, Farzad Beizaee, Fereshteh Shakeri +3
Vision transformers have demonstrated remarkable success in classification by leveraging global self-attention to capture long-range dependencies. However, this same mechanism can…
Histopath-C: Towards Realistic Domain Shifts for Histopathology Vision-Language Adaptation
Mehrdad Noori, Gustavo Adolfo Vargas Hakim, David Osowiechi +6
Medical Vision-language models (VLMs) have shown remarkable performances in various medical imaging domains such as histo\-pathology by leveraging pre-trained, contrastive models t…
Few-Shot, Now for Real: Medical VLMs Adaptation without Balanced Sets or Validation
Julio Silva-RodrÃguez, Fereshteh Shakeri, Houda Bahig +2
Vision-language models (VLMs) are gaining attention in medical image analysis. These are pre-trained on large, heterogeneous data sources, yielding rich and transferable representa…
UNEM: UNrolled Generalized EM for Transductive Few-Shot Learning
Long Zhou, Fereshteh Shakeri, Aymen Sadraoui +3
Transductive few-shot learning has recently triggered wide attention in computer vision. Yet, current methods introduce key hyper-parameters, which control the prediction statistic…
Few-shot Adaptation of Medical Vision-Language Models
Fereshteh Shakeri, Yunshi Huang, Julio Silva-RodrÃguez +4
Integrating image and text data through multi-modal learning has emerged as a new approach in medical imaging research, following its successful deployment in computer vision. Whil…
Boosting Vision-Language Models for Histopathology Classification: Predict all at once
Maxime Zanella, Fereshteh Shakeri, Yunshi Huang +2
The development of vision-language models (VLMs) for histo-pathology has shown promising new usages and zero-shot performances. However, current approaches, which decompose large s…