  MOTIVATION: Detection of prostate cancer during transrectal ultrasound-guided
biopsy is challenging. The highly heterogeneous appearance of cancer, presence
of ultrasound artefacts, and noise all contribute to these difficulties. Recent
advancements in high-frequency ultrasound imaging - micro-ultrasound - have
drastically increased the capability of tissue imaging at high resolution. Our
aim is to investigate the development of a robust deep learning model
specifically for micro-ultrasound-guided prostate cancer biopsy. For the model
to be clinically adopted, a key challenge is to design a solution that can
confidently identify the cancer, while learning from coarse histopathology
measurements of biopsy samples that introduce weak labels. METHODS: We use a
dataset of micro-ultrasound images acquired from 194 patients, who underwent
prostate biopsy. We train a deep model using a co-teaching paradigm to handle
noise in labels, together with an evidential deep learning method for
uncertainty estimation. We evaluate the performance of our model using the
clinically relevant metric of accuracy vs. confidence. RESULTS: Our model
achieves a well-calibrated estimation of predictive uncertainty with area under
the curve of 88$\%$. The use of co-teaching and evidential deep learning in
combination yields significantly better uncertainty estimation than either
alone. We also provide a detailed comparison against state-of-the-art in
uncertainty estimation.
