Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
Artificial intelligence (AI) methods have multiplied our capabilities to extract quantitative
information from digital histopathology images. AI is expected to reduce workload for human …
information from digital histopathology images. AI is expected to reduce workload for human …
Transformers in medical imaging: A survey
Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …
successfully applied to several computer vision problems, achieving state-of-the-art results …
Multimodal learning with transformers: A survey
Transformer is a promising neural network learner, and has achieved great success in
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
various machine learning tasks. Thanks to the recent prevalence of multimodal applications …
Scaling up your kernels to 31x31: Revisiting large kernel design in cnns
We revisit large kernel design in modern convolutional neural networks (CNNs). Inspired by
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …
recent advances in vision transformers (ViTs), in this paper, we demonstrate that using a few …
Daformer: Improving network architectures and training strategies for domain-adaptive semantic segmentation
As acquiring pixel-wise annotations of real-world images for semantic segmentation is a
costly process, a model can instead be trained with more accessible synthetic data and …
costly process, a model can instead be trained with more accessible synthetic data and …
Do vision transformers see like convolutional neural networks?
Convolutional neural networks (CNNs) have so far been the de-facto model for visual data.
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …
Recent work has shown that (Vision) Transformer models (ViT) can achieve comparable or …
Understanding the robustness in vision transformers
Recent studies show that Vision Transformers (ViTs) exhibit strong robustness against
various corruptions. Although this property is partly attributed to the self-attention …
various corruptions. Although this property is partly attributed to the self-attention …
Are transformers more robust than cnns?
Transformer emerges as a powerful tool for visual recognition. In addition to demonstrating
competitive performance on a broad range of visual benchmarks, recent works also argue …
competitive performance on a broad range of visual benchmarks, recent works also argue …
Intriguing properties of vision transformers
Vision transformers (ViT) have demonstrated impressive performance across numerous
machine vision tasks. These models are based on multi-head self-attention mechanisms that …
machine vision tasks. These models are based on multi-head self-attention mechanisms that …
Exploring the limits of out-of-distribution detection
Near out-of-distribution detection (OOD) is a major challenge for deep neural networks. We
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …
demonstrate that large-scale pre-trained transformers can significantly improve the state-of …