A Principled Approach to Failure Analysis and Model Repairment: Demonstration in Medical Imaging

TL;DR

This paper introduces a principled framework for failure analysis and model repair in machine learning, validated on medical imaging tasks, emphasizing meaningful failure type identification and effective model correction.

Contribution

It proposes a standardized, principled approach to failure analysis and model repair, including metrics for failure subtype validation and repair effectiveness.

Findings

Validated framework on classification and object detection tasks.

Metrics effectively distinguish failure subtypes and measure repair success.

Framework improves model robustness in medical imaging applications.

Abstract

Machine learning models commonly exhibit unexpected failures post-deployment due to either data shifts or uncommon situations in the training environment. Domain experts typically go through the tedious process of inspecting the failure cases manually, identifying failure modes and then attempting to fix the model. In this work, we aim to standardise and bring principles to this process through answering two critical questions: (i) how do we know that we have identified meaningful and distinct failure types?; (ii) how can we validate that a model has, indeed, been repaired? We suggest that the quality of the identified failure types can be validated through measuring the intra- and inter-type generalisation after fine-tuning and introduce metrics to compare different subtyping methods. Furthermore, we argue that a model can be considered repaired if it achieves high accuracy on the failure types while retaining performance on the previously correct data. We combine these two ideas into a principled framework for evaluating the quality of both the identified failure subtypes and model repairment. We evaluate its utility on a classification and an object detection tasks. Our code is available at https://github.com/Rokken-lab6/Failure-Analysis-and-Model-Repairment