VET-DINO: Learning Anatomical Understanding Through Multi-View Distillation in Veterinary Imaging

TL;DR

VET-DINO introduces a self-supervised learning framework for veterinary imaging that utilizes multi-view radiographs to learn view-invariant anatomical features and implied 3D understanding, outperforming traditional single-view methods.

Contribution

The paper presents a novel multi-view distillation approach tailored for veterinary imaging, leveraging real multi-view data to enhance anatomical understanding in deep neural networks.

Findings

Achieves state-of-the-art results on veterinary imaging tasks.

Demonstrates superior anatomical understanding from multi-view learning.

Outperforms synthetic augmentation methods in downstream tasks.

Abstract

Self-supervised learning has emerged as a powerful paradigm for training deep neural networks, particularly in medical imaging where labeled data is scarce. While current approaches typically rely on synthetic augmentations of single images, we propose VET-DINO, a framework that leverages a unique characteristic of medical imaging: the availability of multiple standardized views from the same study. Using a series of clinical veterinary radiographs from the same patient study, we enable models to learn view-invariant anatomical structures and develop an implied 3D understanding from 2D projections. We demonstrate our approach on a dataset of 5 million veterinary radiographs from 668,000 canine studies. Through extensive experimentation, including view synthesis and downstream task performance, we show that learning from real multi-view pairs leads to superior anatomical understanding compared to purely synthetic augmentations. VET-DINO achieves state-of-the-art performance on various veterinary imaging tasks. Our work establishes a new paradigm for self-supervised learning in medical imaging that leverages domain-specific properties rather than merely adapting natural image techniques.