A 23 year old Asian female presented with swelling of right knee joint for 5 years with history of exacerbations and remissions of symptoms. She was initially diagnosed as a case of suprapatellar bursitis based on clinical and X-ray findings. Further evaluation with higher imaging modalities was pathognomonic of lipoma arborescens. Patient underwent synovectomy and the diagnosis was confirmed histologically. We describe a histologically proven case of lipoma arborescens to highlight the imaging findings on X-ray, Ultrasound and Magnetic resonance imaging with arthroscopic correlation. The unique feature of this case report is multimodality imaging correlation with arthroscopy and histopathology findings. We have highlighted the pathognomonic imaging findings of this rare but benign intra-articular lesion and also discussed the differential diagnosis in detail.

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The classification of tumours into benign and malignant continues to date to be a very relevant and significant research topic in the cancer research domain. With the advent of Computer Vision and rapid developments in the fields of deep learning, as well as medical devices and instruments, researchers are therefore utilizing the state‐of‐the‐art deep learning architectures to discover patterns in the medical image data and thereby use this information to detect tumours and classify them as benign or malignant. In this paper, we propose a custom state‐of‐the‐art deep learning architecture, the Inception‐ResNet v2 for the classification of ovarian tumours into the two categories of benign and malignant based on a custom dataset with a validation accuracy of 97.5% and a test accuracy of 67%. Furthermore, a quantum convolutional neural network (QCNN) was also implemented with an accuracy of 92% on the validation dataset.

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Medical image analysis and disease diagnosis have significantly improved with the use of AI and Machine Learning algorithms. Automated systems for medical image analysis will help the doctors and radiologists understand the anomaly in a short span of time and with better visualization. Such automated systems will help to reduce the time taken for diagnosis by experts. Recently, Computer Vision is industrialized with the advancements in algorithms and hardware. The proposed study aims to develop a computer vision solution for automatic segmentation and classification of ovarian tumours in discriminating between benign and malignant tumours by image‐to‐image translation approach using Conditional Generative Adversarial Network (cGAN). Our method uses a novel algorithm which segments and classifies the images in a single pipeline which makes the algorithm unique and useful. This research also aims to compare its diagnostic accuracy with that of an expert radiologist. The dataset used by in the present study is formulated with images obtained from a hospital and annotated by doctors from the hospital. The obtained results show the proposed study is promising for ovarian cancer segmentation and classification with an average segmentation score of 0.825 for benign and 0.765 for malignant and classification accuracy of 83% for benign and 79% for malignant, precision score of 85% for benign and 80% for malignant and F1 score of 81% for benign and 80.1% for malignant images. The proposed methodology is evaluated on the existing MRI images to perform segmentation and classification. The results obtained shows that the proposed methodology can perform well on other MRI images. In this study, proposed methodology is convenient as separate segmentation need not be done and is giving good result. The same MRI images are segmented using UNet and classified using RESNET 101 and results are compared with the proposed methodology.