ePillID Dataset: A Low-Shot Fine-Grained Benchmark for Pill Identification

Identifying prescription medications is a frequent task for patients and medical professionals; however, this is an error-prone task as many pills have similar appearances (e.g. white round pills), which increases the risk of medication errors. In this paper, we introduce ePillID, the largest public benchmark on pill image recognition, composed of 13k images representing 8184 appearance classes (two sides for 4092 pill types). For most of the appearance classes, there exists only one reference image, making it a challenging low-shot recognition setting. We present our experimental setup and evaluation results of various baseline models on the benchmark. The best baseline using a multi-head metric-learning approach with bilinear features performed remarkably well; however, our error analysis suggests that they still fail to distinguish particularly confusing classes.

1. Introduction

At least 1.5 million preventable adverse drug events (ADE) occur each year in the U.S. [22]. Medication errors related to ADEs can occur while writing or filling prescriptions, or even when taking or managing medications [1]. For example, pharmacists must verify thousands of pills dispensed daily in the pharmacy, a process that is largely manual and prone to error. Chronically-ill and elderly patients often separate pills from their original prescription bottles, which can lead to confusion and misadministration. Many errors are preventable; however, pills with visually similar characteristics are difficult to identify or distinguish, increasing error potential.