PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models

Table of Contents

Title: PFPs: Prompt-guided Flexible Pathological Segmentation for Diverse Potential Outcomes Using Large Vision and Language Models

Authors: Can Cui, Ruining Deng, Junlin Guo, Quan Liu, Tianyuan Yao, Haichun Yang, Yuankai Huo

Published: Jul 13 2024

Summary:

Authors proposed a method called PFPs that increases a potential and flexibility of the efficient segment anything model (EfficientSAM, Xiong et al., 2024) for pathology image segmentation tasks.
They was inspired by Omni-seg (Deng et al., 2023) and HATs (Deng et al., 2024).
Low-rank adaptation (LoRA, Hu et al., 2021) was used for fine-tuning of pre-trained large language model (LLM) called TinyLLaMA (Zhang et al., 2024).
Dataset: a kidney dataset NEPTUNE (Barisoni et al., 2013).
They define 9 types of tasks such as “Segmentation of the nuclei outside the capsule region”.
What I learned: Segment anything model (SAM, Kirillov, 2023), dynamic head concept in Omni-seg and HATs.

Summary (Generated by Microsoft Copilot):

Introduction:

The paper explores the use of Vision Foundation Models and Large Language Models (LLMs) for flexible pathological image segmentation.

Challenges:

Current models lack flexibility and precision in segmenting diverse and complex structures in pathology images.

Methods:

The proposed method integrates language prompts with spatial annotations using EfficientSAM and TinyLlama-1.1B models.

Novelties:

Introduction of a computational-efficient pipeline using fine-tuned language prompts for multi-class segmentation.

Results:

The approach shows improved flexibility and accuracy in segmenting kidney pathology images.

Performances:

The model’s performance is evaluated using Dice scores, showing better results with complete training sets.

Limitations:

Discussion:

Future research aims to incorporate more diverse language prompts and larger datasets for better generalization.