Abstract
Deep convolutional networks have become a powerful tool for medical imaging diagnostic. In pathology, most efforts have been focused in the subfield of histology, while cytopathology (which studies diagnostic tools at the cellular level) remains underexplored. In this paper, we propose a novel deep learning model for cancer detection from urinary cytopathology screening images. We leverage recent ideas from the field of multioutput neural networks to provide a model that can efficiently train even on small-scale datasets, such as those typically found in real-world scenarios. Additionally, we argue that calibration (i.e., providing confidence levels that are aligned with the ground truth probability of an event) has been a major shortcoming of prior works, and we experiment a number of techniques to provide a well-calibrated model. We evaluate the proposed algorithm on a novel dataset, and we show that the combination of focal loss, multiple outputs, and temperature scaling provides a model that is significantly more accurate and calibrated than a baseline deep convolutional network.
Highlights
Bladder cancer is a widely diffused life-threatening risk for people in between 65 and 84 years of age, with men being targeted two to three times more than women [1]
This paper explores the usefulness of deep convolutional neural networks (CNNs) in the context of automatic detection of urothelial cancer cells from Urinary cytology (UC) screenings
We show on our UC dataset and on a benchmark cell classification dataset that our multiexit, calibrated deep CNN strongly outperforms a baseline CNN both in terms of accuracy and in terms of calibration in all scenarios that we considered
Summary
Bladder cancer is a widely diffused life-threatening risk for people in between 65 and 84 years of age, with men being targeted two to three times more than women [1]. In Italy, it is the fourth most common cancer in men (12%) and the fifth most common in the total population (8%), with 5year survival estimated to be around 79% (http://www .salute.gov.it, 2019). Its main purpose is the surveillance and detection of urothelial neoplasms, with its strength being its specificity for the detection of high-grade carcinomas. Today the examination of UT specimens in the cytopathology laboratory is typically used to screen for urothelial neoplasms in two populations: patients with newonset and patients with a history of urothelial neoplasia, as urothelial carcinomas have high recurrence rates [3]. UC samples constitute a significant percentage of daily nongynecologic cases in any cytopathology laboratory and are one of the most difficult specimens that pathologists encounter [4]
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