Glomeruli are histological structures located at the beginning of the nephrons in the kidney, having primary importance in diagnosing many renal diseases. Classifying glomerular lesions is time-consuming and requires experienced pathologists. Hence automatic classification methods can support pathologists in the diagnosis and decision-making scenarios. Recently most of state-of-the-art medical imaging classification methods have been based on deep-learning approaches, which are prone to return overconfident scores, even for out-of-distribution (OOD) inputs. Determining whether inputs are OOD samples is of underlying importance so as to ensure the safety and robustness of critical machine learning applications. Bare this in mind, we propose a unified framework comprised of unbounded open-set recognition and multi-lesion glomerular classification (membranous nephropathy, glomerular hypercellularity, and glomerular sclerosis). Our proposed framework classifies the input into in- or OOD data: If the sample is an OOD image, the input is disregarded, indicating that the model “doesn’t know” the class; otherwise, if the sample is classified as in-distribution, an uncertainty method based on Monte-Carlo dropout is used for multi-lesion classification. We explored an energy-based approach that allows open-set recognition without fine-tuning the in-distribution weights to specific OOD data. Ultimately, our results suggest that uncertainty estimation methods (Monte-Carlo dropout, test-time data augmentation, and ensemble) combined with energy scores slightly improved our open-set recognition for in-out classification. Our results also showed that this improvement was achieved without decreasing the 4-lesion classification performance, with an F1-score of 0.923. Toward an unbounded open-set glomerular multi-lesion recognition, the proposed method also kept a competitive performance.
Panoramic X-rays are an essential tool to assist dentistry experts in their diagnostic procedures. Dentists can analyze the anatomical and pathological structures while planing orthodontic, periodontal, and surgical treatments. Even though detecting, numbering, and segmenting teeth are essential tasks to leverage automatic analysis on panoramic X-rays, it is lacking in the literature a study and a data set that considers at the same time deciduous and permanent teeth in a wide variety of panoramic X-rays. To fill this gap, this work introduces a novel, challenging, and high-variable public data set labeled from scratch. This data set incorporates new elements such as instance overlapping and deciduous teeth, supporting our study on tooth numbering and segmentation. Our efforts aim to improve the segmentation on the boundaries because they are the main hurdle of the instance segmentation methods. For that, we investigate and compare (quantitatively and qualitatively) two Mask R-CNN-based solutions: the standard one, with a fully convolutional network, and another one that employs the PointRend module on the top. Our findings attest to the feasibility of extending segmentation and numbering to deciduous teeth through end-to-end deep learning architectures, as well as, the higher performance of the Mask R-CNN with PointRend either on instance segmentation (mAP of +2 percentage points) or the numbering (mAP of +1.2 percentage points) on the test data set. We hope that our findings and our new data set support the development of new tools to assist professionals in faster diagnosis, making upon panoramic X-rays.
Glomeruli are microscopic structures of the kidney affected in many renal diseases. The diagnosis of these diseases depends on the study by a pathologist of each glomerulus sampled by renal biopsy. To help pathologists with the image analysis, we propose a glomerulus detection method on renal histological images. For that, we evaluated two state-ofthe-art deep-learning techniques: single shot multibox detector with Inception V2 (SI2) and faster region-based convolutional neural network with Inception V2 (FRI2). As a result, we reached: 0.88 of mAP and 0.94 of F1-score, when using SI2, and 0.87 of mAP and 0.97 of F1-score, when using FRI2. On average, to process each image, FRI2 required 30.91s, while SI2 just 0.79s. In our experiments, we found that SI2 model is the best detection method for our task since it is 64% faster in the training stage and 98% faster to detect the glomeruli in each image.
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