Remote Pathological Gait Classification System

• URL: http://arxiv.org/abs/2105.01634v1
• Date: Tue, 4 May 2021 17:21:29 GMT
• Title: Remote Pathological Gait Classification System
• Authors: Pedro Albuquerque, Joao Machado, Tanmay Tulsidas Verlekar, Luis Ducla Soares, Paulo Lobato Correia
• Abstract summary: Gait analysis can be used to detect impairments and help diagnose illnesses and assess patient recovery. The biggest publicly available pathological gait dataset contains only 10 subjects, simulating 4 gait pathologies. This paper presents a new dataset called GAIT-IT, captured from 21 subjects simulating 4 gait pathologies, with 2 severity levels, besides normal gait.
• Score: 3.8098589140053862
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Several pathologies can alter the way people walk, i.e. their gait. Gait analysis can therefore be used to detect impairments and help diagnose illnesses and assess patient recovery. Using vision-based systems, diagnoses could be done at home or in a clinic, with the needed computation being done remotely. State-of-the-art vision-based gait analysis systems use deep learning, requiring large datasets for training. However, to our best knowledge, the biggest publicly available pathological gait dataset contains only 10 subjects, simulating 4 gait pathologies. This paper presents a new dataset called GAIT-IT, captured from 21 subjects simulating 4 gait pathologies, with 2 severity levels, besides normal gait, being considerably larger than publicly available gait pathology datasets, allowing to train a deep learning model for gait pathology classification. Moreover, it was recorded in a professional studio, making it possible to obtain nearly perfect silhouettes, free of segmentation errors. Recognizing the importance of remote healthcare, this paper proposes a prototype of a web application allowing to upload a walking person's video, possibly acquired using a smartphone camera, and execute a web service that classifies the person's gait as normal or across different pathologies. The web application has a user friendly interface and could be used by healthcare professionals or other end users. An automatic gait analysis system is also developed and integrated with the web application for pathology classification. Compared to state-of-the-art solutions, it achieves a drastic reduction in the number of model parameters, which means significantly lower memory requirements, as well as lower training and execution times. Classification accuracy is on par with the state-of-the-art.

Related papers

• GPC: Generative and General Pathology Image Classifier [2.6954348706500766]
  We propose a task-agnostic generative and general pathology image classifier, so called GPC. GPC maps pathology images into a high-dimensional feature space and generates pertinent class labels as texts. We evaluate GPC on six datasets for four different pathology image classification tasks.
  arXiv  Detail & Related papers  (2024-07-12T06:54:31Z)
• CAMP: Continuous and Adaptive Learning Model in Pathology [4.0422818386776855]
  We propose a continuous and adaptive learning model in pathology ( CAMP) for pathology image classification. CAMP is a generative, efficient, and adaptive classification model that can continuously adapt to any classification task. We evaluated CAMP on 22 datasets, including 1,171,526 patches and 11,811 pathology slides, across 17 classification tasks.
  arXiv  Detail & Related papers  (2024-07-12T06:45:37Z)
• Robust and Interpretable Medical Image Classifiers via Concept Bottleneck Models [49.95603725998561]
  We propose a new paradigm to build robust and interpretable medical image classifiers with natural language concepts. Specifically, we first query clinical concepts from GPT-4, then transform latent image features into explicit concepts with a vision-language model.
  arXiv  Detail & Related papers  (2023-10-04T21:57:09Z)
• Towards a Visual-Language Foundation Model for Computational Pathology [5.72536252929528]
  We introduce CONtrastive learning from Captions for Histopathology (CONCH) CONCH is a visual-language foundation model developed using diverse sources of histopathology images, biomedical text, and task-agnostic pretraining. It is evaluated on a suite of 13 diverse benchmarks, achieving state-of-the-art performance on histology image classification, segmentation, captioning, text-to-image and image-to-text retrieval.
  arXiv  Detail & Related papers  (2023-07-24T16:13:43Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction [55.94378672172967]
  We focus on few-shot disease subtype prediction problem, identifying subgroups of similar patients. We introduce meta learning techniques to develop a new model, which can extract the common experience or knowledge from interrelated clinical tasks. Our new model is built upon a carefully designed meta-learner, called Prototypical Network, that is a simple yet effective meta learning machine for few-shot image classification.
  arXiv  Detail & Related papers  (2020-09-02T02:50:30Z)
• A Data-Efficient Deep Learning Based Smartphone Application For Detection Of Pulmonary Diseases Using Chest X-rays [0.0]
  The app inputs Chest X-Ray images captured from the mobile camera which is then relayed to the AI architecture in a cloud platform. Doctors with a smartphone can leverage the application to save the considerable time that standard COVID-19 tests take for preliminary diagnosis.
  arXiv  Detail & Related papers  (2020-08-19T04:28:17Z)
• Hierarchical Reinforcement Learning for Automatic Disease Diagnosis [52.111516253474285]
  We propose to integrate a hierarchical policy structure of two levels into the dialogue systemfor policy learning. The proposed policy structure is capable to deal with diagnosis problem including large number of diseases and symptoms.
  arXiv  Detail & Related papers  (2020-04-29T15:02:41Z)
• Optimal Transfer Learning Model for Binary Classification of Funduscopic Images through Simple Heuristics [0.8370915747360484]
  We attempt to use deep learning neural networks to diagnose funduscopic images, visual representations of the interior of the eye. We propose a unifying model for disease classification: low-cost inference of a fundus image to determine whether it is healthy or diseased.
  arXiv  Detail & Related papers  (2020-02-11T03:49:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.