# Tyche

**Repository Path**: Dh_project/Tyche

## Basic Information

- **Project Name**: Tyche
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2026-04-22
- **Last Updated**: 2026-04-22

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Tyche: Stochastic In-Context Learning for Medical Image Segmentation


Official website of: [_Tyche: Stochastic In-Context Learning for Medical Image Segmentation_](http://arxiv.org/abs/2401.13650).  
[Marianne Rakic](https://mariannerakic.github.io/), [Hallee E. Wong](https://halleewong.github.io/), [Jose Javier Gonzalez Ortiz](https://josejg.com/),  
[Beth Cimini](https://www.broadinstitute.org/bios/beth-cimini), [John V. Guttag](https://people.csail.mit.edu/guttag/) \& [Adrian V. Dalca](https://www.mit.edu/~adalca/)


## Abstract
Existing learning-based solutions to medical image segmentation have two important shortcomings. First, for each new segmentation task, usually a new model has to be trained or fine-tuned. This requires extensive resources and machine-learning expertise, and is therefore often infeasible for medical researchers and clinicians. Second, most existing segmentation methods produce a single deterministic segmentation mask for a given image. However, in practice, there is often considerable uncertainty about what constitutes the _correct_ segmentation, and different expert annotators will often segment the same image differently. We tackle both of these problems with _Tyche_, a model that uses a context set to generate stochastic predictions for previously unseen tasks without the need to retrain. Tyche differs from other in-context segmentation methods in two important ways.  

1. We introduce a novel convolution block architecture that enables interactions among predictions.
2. We introduce in-context test-time augmentation, a new mechanism to provide prediction stochasticity.

When combined with appropriate model design and loss functions, Tyche can predict a set of plausible diverse segmentation candidates for new or unseen medical images and segmentation tasks without the need to retrain.


[*Check out our website* :D](http://tyche.csail.mit.edu/)  (demo included)