Manipulation of Polymorphic Objects Using Two Robotic Arms through CNN Networks

This article presents an interaction system for two 5 DOF (Degrees of Freedom) manipulators with 3-finger grippers, which will be used to grab and displace up to 10 polymorphic objects shaped as pentominoes, inside a VRML (Virtual Reality Modeling Language) environment, by performing element detection and classification using an R-CNN (Region Proposal Convolutional Neural Network), and point detection and gripping orientation using a DAG-CNN (Directed Acyclic Graph-Convolutional Neural Network). It was analyzed the feasibility or not of a grasp is determined depending on how the geometry of an element fits the free space between the gripper fingers. A database was created to be used as training data with each of the grasp positions for the polyshapes, so the network training can be focused on finding the desired grasp positions, enabling any other grasp found to be considered a feasible grasp, and eliminating the need to find additional better grasp points, changing the shape, inclination and angle of rotation. Under varying test conditions, the test successfully achieved gripping of each object with one manipulator and passing it to the second manipulator as part of the grouping process, in the opposite end of the work area, using an R-CNN and a DAG-CNN, with an accuracy of 95.5% and 98.8%, respectively, and performing a geometric analysis of the objects to determine the displacement and rotation required by the gripper for each individual grip

Semantic Segmentation of Histopathological Slides for the Classification of Cutaneous Lymphoma and Eczema

Mycosis fungoides (MF) is a rare, potentially life threatening skin disease, which in early stages clinically and histologically strongly resembles Eczema, a very common and benign skin condition. In order to increase the survival rate, one needs to provide the appropriate treatment early on. To this end, one crucial step for specialists is the evaluation of histopathological slides (glass slides), or Whole Slide Images (WSI), of the patients' skin tissue. We introduce a deep learning aided diagnostics tool that brings a two-fold value to the decision process of pathologists. First, our algorithm accurately segments WSI into regions that are relevant for an accurate diagnosis, achieving a Mean-IoU of 69% and a Matthews Correlation score of 83% on a novel dataset. Additionally, we also show that our model is competitive with the state of the art on a reference dataset. Second, using the segmentation map and the original image, we are able to predict if a patient has MF or Eczema. We created two models that can be applied in different stages of the diagnostic pipeline, potentially eliminating life-threatening mistakes. The classification outcome is considerably more interpretable than using only the WSI as the input, since it is also based on the segmentation map. Our segmentation model, which we call EU-Net, extends a classical U-Net with an EfficientNet-B7 encoder which was pre-trained on the Imagenet dataset.Comment: Submitted to https://link.springer.com/chapter/10.1007/978-3-030-52791-4_

Early detection of peritoneal dialysis complications through convolutional neural networks

La diálisis peritoneal es una alternativa para pacientes con insuficiencia renal crónica, que requiere el análisis periódico del líquido resultante para la detección precoz de complicaciones. Dicho análisis implica la evaluación directa del líquido bajo microscopio y una posterior prueba bioquímica. Como alternativa, el líquido podría analizarse a través de una fotografía (evaluación indirecta) lo que permitiría detectar precozmente una posible complicación, sin que el paciente deba acercarse a un centro de nefrología, mejorando sustancialmente su calidad de vida. En [Comas et al., XX Congreso Argentino de Bioingeniería, pp. 477–486 (2015)] se estudió preliminarmente la detección de muestras patológicas del líquido a partir de fotografías, utilizando descriptores de color y el clasificador k-vecinos más próximos. En el presente trabajo, se presenta un método basado en redes neuronales convolucionales, partiendo de la Alexnet y utilizando transfer learning. La fase de clasificación se implementó con un perceptrón multicapa, clasificando las fotografías entre “normal” y “patológica”, con el resultado de la prueba bioquímica como Gold-standard. Se obtuvo una tasa de error de 5,79%, una FPR de 4,21% y una FNR de 7,37%, con gran estabilidad, reflejada en bajas desviaciones estándar en la estimación de las medidas de error. El método propuesto es más robusto que el enfoque previo, sin requerir ningún tipo de preprocesamiento, ni extracción de características, siendo un buen punto de partida para el desarrollo de una herramienta automática con adecuada capacidad de soporte al diagnóstico.Peritoneal dialysis is an alternative for patients with chronic renal failure requiring periodic analysis of the resulting liquid for the early detection of complications, which involves a direct evaluation of the liquid under a microscope and a biochemical test. Alternatively, the liquid could be analyzed through a photograph (indirect evaluation), enabling the early detection of complications, without requiring the patient going to a nephrology center, improving their life quality. In [Comas et al., XX Congreso Argentino de Bioingeniería, pp. 477–486 (2015)], detection of pathological samples of the liquid from photographs was preliminary studied using color descriptors and k-nearest neighbors as classifier. In the present paper, a method based on convolutional neural networks is presented, starting from Alexnet and using transfer learning. The classification phase was implemented with a multilayer perceptron, classifying the photographs between “normal” and “pathological”, using the biochemical test as Gold-standard. An error rate of 5.79%, a FPR of 4.21% and a FNR of 7.37% were obtained with great stability, reflected in low standard deviations in the estimation of the error measures. The proposed method is more robust than the previous approach, without requiring any preprocessing or feature extraction, being a good starting point for the development of an automatic tool with adequate diagnostic capacity.Fil: Comas, Diego Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Meschino, Gustavo Javier. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Ballarin, Virginia Laura. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica. Universidad Nacional de Mar del Plata. Facultad de Ingeniería. Instituto de Investigaciones Científicas y Tecnológicas en Electrónica; ArgentinaFil: Jerónimo Aguilera Díaz. Hospital Italiano; ArgentinaFil: Musso, Carlos. Hospital Italiano; ArgentinaFil: Rivera, Héctor. Hospital Italiano; ArgentinaFil: Plazzotta, Fernando. Hospital Italiano; ArgentinaFil: Algranati, Luis. Hospital Italiano; ArgentinaFil: Luna, Daniel. Hospital Italiano; Argentin

Computational methods for metastasis detection in lymph nodes and characterization of the metastasis-free lymph node microarchitecture: A systematic-narrative hybrid review

Background Histological examination of tumor draining lymph nodes (LNs) plays a vital role in cancer staging and prognostication. However, as soon as a LN is classed as metastasis-free, no further investigation will be performed and thus, potentially clinically relevant information detectable in tumor-free LNs is currently not captured. Objective To systematically study and critically assess methods for the analysis of digitized histological LN images described in published research. Methods A systematic search was conducted in several public databases up to December 2023 using relevant search terms. Studies using brightfield light microscopy images of hematoxylin and eosin or immunohistochemically stained LN tissue sections aiming to detect and/or segment LNs, their compartments or metastatic tumor using artificial intelligence (AI) were included. Dataset, AI methodology, cancer type, and study objective were compared between articles. Results A total of 7201 articles were collected and 73 articles remained for detailed analyses after article screening. Of the remaining articles, 86% aimed at LN metastasis identification, 8% aimed at LN compartment segmentation, and remaining focused on LN contouring. Furthermore, 78% of articles used patch classification and 22% used pixel segmentation models for analyses. Five out of six studies (83%) of metastasis-free LNs were performed on publicly unavailable datasets, making quantitative article comparison impossible. Conclusions Multi-scale models mimicking multiple microscopy zooms show promise for computational LN analysis. Large-scale datasets are needed to establish the clinical relevance of analyzing metastasis-free LN in detail. Further research is needed to identify clinically interpretable metrics for LN compartment characterization

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201

Intelligent Methods based on Machine Learning for Classification and Identification of Objects of Interest: A Laboratory Task

Podstatou diplomové práce je návrh exemplárních laboratorních úloh, jejichž cílem je seznámit studující s klasifikací dat za pomocí neuronových sítí. Jednotlivé úlohy se zabývají klasifikací dat. První úkol se věnuje základní metodě klasifikace pomocí perceptronu. Další úkoly se věnují jednak metodě optimalizace neuronové sítě pomocí genetických algoritmů a jednak využití konvolučních neuronových sítí pro klasifikaci jednorozměrných akustických signálů a dvourozměrných obrazů. V jednotlivých úlohách jsou data, pro natrénovaní neuronových sítí, buď dynamicky vytvořena nebo načtena při startu. Následně úlohy demonstrují vytvoření jednotlivých sítí včetně způsobu jejich natrénovaní dle různých vstupních parametrů. V posledních krocích laboratorních úloh se algoritmy validují a výsledky analyzují. Všechny algoritmy dílčích částí byly naprogramovány v prostředí MATLAB s využitím technologie Live Script.The aim of the master thesis is to design exemplary laboratory tasks to introduce students to data classification with neural networks. The individual tasks deal with data classification. The first assignment is about the basic method of classification using perceptron. Other tasks focus on the neural network optimization method using genetic algorithms and the next assignment deals with the use of convolutional neural networks for classification of one-dimensional acoustic signals and two-dimensional images. For each task, data is created or loaded to train the neural networks, then create the network and train it under different settings. In the last step of the lab tasks, the algorithms are validated, and the results are analysed. All the algorithms of the subsections were programmed in MATLAB using Live Script technology.450 - Katedra kybernetiky a biomedicínského inženýrstvívelmi dobř