Annotation-Efficient Deep Semi-Supervised Learning for Automatic Knee Osteoarthritis Severity Diagnosis from Plain Radiographs

Osteoarthritis (OA) is a worldwide disease that occurs in joints causing irreversible damage to cartilage and other joint tissues. The knee is particularly vulnerable to OA, and millions of people, regardless of gender, geographical location, and race, suffer from knee OA. When the disease reaches the late stages, patients have to undergo a total knee replacement (TKR) surgery to avoid disability. For society, direct and indirect costs of OA are high, and for instance, OA is one of the five most expensive healthcare expenditures in Europe. In the United States, the burden of knee OA is also high, and TKR surgeries annually cost over 10 billion dollars. If knee OA could be detected at an early stage, its progression might be slowed down, thereby yielding significant benefits at personal and societal levels. Radiographs, low-cost and widely available in primary care, are sufficiently informative for knee OA severity diagnosis. However, the process of visual assessment of radiographs is rather tedious, and as a result, various Deep Learning (DL) based methods for automatic diagnosis of knee OA severity have recently been developed. The primary drawback of these methods is their dependency on large amounts of annotations, which are expensive in terms of cost and time to collect.
In this paper, we introduce Semixup, a novel Semi-Supervised Learning (SSL) method, which we apply for to automatic diagnosis of the knee OA severity in an annotation-efficient manner.