Medical Waste Sorting: a computer vision approach for assisted primary sorting

• URL: http://arxiv.org/abs/2303.04720v2
• Date: Tue, 18 Apr 2023 12:29:54 GMT
• Title: Medical Waste Sorting: a computer vision approach for assisted primary sorting
• Abstract summary: We propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Medical waste, i.e. waste produced during medical activities in hospitals, clinics and laboratories, represents hazardous waste whose management involves special care and high costs. However, this kind of waste contains a significant fraction of highly valued materials that can enter a circular economy process. To this end, in this paper, we propose a computer vision approach for assisting in the primary sorting of medical waste. The feasibility of our approach is demonstrated on a representative dataset we collected and made available to the community, with which we have trained a model that achieves 100\% accuracy, and a new dataset on which the trained model exhibits good generalization.

Related papers

• Learning from waste: Machine Learning for health risk prediction and computer vision-based sorting in Ghana [0.0]
  Poor sanitation and improper waste management practices contribute to substantial economic costs and avoidable deaths annually.<n>This study extends that investigation using two data-driven approaches.<n>A Random Forest classifier was developed to predict illness categories using waste disposal practices and demographic survey data.<n>A MobileNetV2 image classification model enabled automated waste sorting via visual recognition, achieving 88.2% accuracy and a macro F1 score of 0.87 on the test set.
  arXiv  Detail & Related papers  (2026-08-26T13:06:35Z)
• Project Imaging-X: A Survey of 1000+ Open-Access Medical Imaging Datasets for Foundation Model Development [314.80153557710616]
  We present the largest survey to date of medical image datasets, covering over 1,000 open-access datasets.<n>Our analysis exposes a landscape that is modest in scale, fragmented across narrowly scoped tasks, and unevenly distributed across organs and modalities.<n>We propose a metadata-driven fusion paradigm (MDFP) that integrates public datasets with shared modalities or tasks.
  arXiv  Detail & Related papers  (2026-03-29T00:46:53Z)
• OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning [26.144214758141874]
  We investigate strategies for training and data curation to develop a robust multimodal reasoning model in the medical domain.<n>We scale experiments to a dataset of over 8 million examples and 6.8 billion response tokens.<n>Our results indicate that curating a high-quality, diverse training dataset with varying structured reasoning trace lengths enables the fine-tuned model to self-calibrate its reasoning trajectory lengths.
  arXiv  Detail & Related papers  (2025-11-28T15:21:51Z)
• Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning [57.873833577058]
  We build a multimodal dataset enriched with extensive medical knowledge.<n>We then introduce our medical-specialized MLLM: Lingshu.<n>Lingshu undergoes multi-stage training to embed medical expertise and enhance its task-solving capabilities.
  arXiv  Detail & Related papers  (2025-06-08T08:47:30Z)
• STLLaVA-Med: Self-Training Large Language and Vision Assistant for Medical Question-Answering [58.79671189792399]
  STLLaVA-Med is designed to train a policy model capable of auto-generating medical visual instruction data. We validate the efficacy and data efficiency of STLLaVA-Med across three major medical Visual Question Answering (VQA) benchmarks.
  arXiv  Detail & Related papers  (2024-06-28T15:01:23Z)
• OntoMedRec: Logically-Pretrained Model-Agnostic Ontology Encoders for Medication Recommendation [33.82146975306507]
  We propose OntoMedRec, a logically-pretrained and model-agnostic medical Ontology for Medication Recommendation. We conduct comprehensive experiments on benchmark datasets to evaluate the effectiveness of OntoMedRec.
  arXiv  Detail & Related papers  (2024-01-29T00:29:39Z)
• Visual Material Characteristics Learning for Circular Healthcare [1.4957306171002251]
  We make a step towards circular healthcare by developing vision systems targeting three main circular economy tasks. The performance of our systems demonstrates that representation-learning vision can improve the recovery chain. We also published two fully-annotated datasets for image segmentation and for key-point tracking in disassembly operations of inhalers and glucose meters.
  arXiv  Detail & Related papers  (2023-09-09T11:31:40Z)
• 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)
• Learnable Weight Initialization for Volumetric Medical Image Segmentation [66.3030435676252]
  We propose a learnable weight-based hybrid medical image segmentation approach. Our approach is easy to integrate into any hybrid model and requires no external training data. Experiments on multi-organ and lung cancer segmentation tasks demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-06-15T17:55:05Z)
• Distantly supervised end-to-end medical entity extraction from electronic health records with human-level quality [77.34726150561087]
  We propose a novel method of doing medical EE from electronic health records ( EHR) as a single-step multi-label classification task. Our model is trained end-to-end in a distantly supervised manner using targets automatically extracted from medical knowledge base. Our work demonstrates that medical entity extraction can be done end-to-end without human supervision and with human quality given the availability of a large enough amount of unlabeled EHR and a medical knowledge base.
  arXiv  Detail & Related papers  (2022-01-25T17:04:46Z)
• ZeroWaste Dataset: Towards Automated Waste Recycling [51.053682077915546]
  We present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. This dataset contains over1800fully segmented video frames collected from a real waste sorting plant. We show that state-of-the-art segmentation methods struggle to correctly detect and classify target objects.
  arXiv  Detail & Related papers  (2021-06-04T22:17:09Z)
• BiteNet: Bidirectional Temporal Encoder Network to Predict Medical Outcomes [53.163089893876645]
  We propose a novel self-attention mechanism that captures the contextual dependency and temporal relationships within a patient's healthcare journey. An end-to-end bidirectional temporal encoder network (BiteNet) then learns representations of the patient's journeys. We have evaluated the effectiveness of our methods on two supervised prediction and two unsupervised clustering tasks with a real-world EHR dataset.
  arXiv  Detail & Related papers  (2020-09-24T00:42:36Z)

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.