GenSelfDiff-HIS: Generative Self-Supervision Using Diffusion for Histopathological Image Segmentation

by   Vishnuvardhan Purma, et al.

Histopathological image segmentation is a laborious and time-intensive task, often requiring analysis from experienced pathologists for accurate examinations. To reduce this burden, supervised machine-learning approaches have been adopted using large-scale annotated datasets for histopathological image analysis. However, in several scenarios, the availability of large-scale annotated data is a bottleneck while training such models. Self-supervised learning (SSL) is an alternative paradigm that provides some respite by constructing models utilizing only the unannotated data which is often abundant. The basic idea of SSL is to train a network to perform one or many pseudo or pretext tasks on unannotated data and use it subsequently as the basis for a variety of downstream tasks. It is seen that the success of SSL depends critically on the considered pretext task. While there have been many efforts in designing pretext tasks for classification problems, there haven't been many attempts on SSL for histopathological segmentation. Motivated by this, we propose an SSL approach for segmenting histopathological images via generative diffusion models in this paper. Our method is based on the observation that diffusion models effectively solve an image-to-image translation task akin to a segmentation task. Hence, we propose generative diffusion as the pretext task for histopathological image segmentation. We also propose a multi-loss function-based fine-tuning for the downstream task. We validate our method using several metrics on two publically available datasets along with a newly proposed head and neck (HN) cancer dataset containing hematoxylin and eosin (H&E) stained images along with annotations. Codes will be made public at


Hierarchical Self-Supervised Learning for Medical Image Segmentation Based on Multi-Domain Data Aggregation

A large labeled dataset is a key to the success of supervised deep learn...

DiffInfinite: Large Mask-Image Synthesis via Parallel Random Patch Diffusion in Histopathology

We present DiffInfinite, a hierarchical diffusion model that generates a...

A Dual-branch Self-supervised Representation Learning Framework for Tumour Segmentation in Whole Slide Images

Supervised deep learning methods have achieved considerable success in m...

AVES: Animal Vocalization Encoder based on Self-Supervision

The lack of annotated training data in bioacoustics hinders the use of l...

Unsupervised Dense Nuclei Detection and Segmentation with Prior Self-activation Map For Histology Images

The success of supervised deep learning models in medical image segmenta...

Deshadow-Anything: When Segment Anything Model Meets Zero-shot shadow removal

Segment Anything (SAM), an advanced universal image segmentation model t...

Improving Sketch Colorization using Adversarial Segmentation Consistency

We propose a new method for producing color images from sketches. Curren...

Please sign up or login with your details

Forgot password? Click here to reset