Distinguishing between benign and malignant ovarian cysts is a challenging task that depends on subjective visual markers in ultrasound scans. Current manual methods remain prone to costly misdiagnoses and the application of these methods depend heavily on the clinician's level of expertise. Recent research demonstrates promising applications of Convolutional Neural Networks (CNNs) for ovarian tumor classification; however, we observed that their performance is limited when applied to a diverse and complex dataset. To address this, we propose, implement, and evaluate three improvements to a baseline classifier.

First, we use a deep learning-based approach to remove burned-in medical annotations and introduce a weighted mean squared error (MSE) loss to improve its effectiveness by emphasizing relevant regions. This aims to better recover the original image content prior to annotation and remove annotations which can act as confounders. Second, we enhance classification by fusing image features with two readily available clinical factors at an intermediate stage of the network. Third, and central to this study, we incorporate a segmentation path that acts as a regularizer, encouraging the shared encoder to learn lesion-specific features that benefit the classification head.

These three contributions are informed by domain-specific knowledge of ovarian lesions and collectively demonstrate promising directions for improving deep learning-based models in this setting.

Convolutional Neural Networks (CNNs) have made significant strides in the field of image processing over the last decade. Different approaches have been taken and improvements have been suggested. This paper looks at a newer novelty to neural networks for image counting, which is based on single-pixel center localization instead of the traditional bounding boxes. This neural network’s loss function is the weighted average Hausdorff distance, which does not only take into account the number of misclassified points but also the distance between predicted points and ground truth values. The paper aims to compare the accuracy of the single-pixel center neural network on original training images of wheat heads as compared to filtered images. The filtered images have had a band pass filter applied to them, that is constructed by looking at the average frequency of wheat heads. It filters out certain lower and higher frequencies up to a threshold, and its aim is to reduce background noise and accentuate the wheat heads. Results showed that there was no significant and attributable improvement in the performance of the object counter when trained on images with filtered frequency information. A discussion of the unexpected results then carries out, with the aim of rationalizing the insignificant improvement in performance of the neural network on filtered images. As part of the discussion and conclusion, a recommendation is also made, giving insights into determining if this single-pixel center neural network is appropriate for a given dataset of images.