TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos

• URL: http://arxiv.org/abs/2204.08166v1
• Date: Mon, 18 Apr 2022 05:14:27 GMT
• Title: TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos
• Authors: Shuojia Zou, Chen Li, Hongzan Sun, Peng Xu, Jiawei Zhang, Pingli Ma, Yudong Yao, Xinyu Huang, Marcin Grzegorzek
• Abstract summary: We present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.
• Score: 17.739265119524244
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: The detection of tiny objects in microscopic videos is a problematic point, especially in large-scale experiments. For tiny objects (such as sperms) in microscopic videos, current detection methods face challenges in fuzzy, irregular, and precise positioning of objects. In contrast, we present a convolutional neural network for tiny object detection (TOD-CNN) with an underlying data set of high-quality sperm microscopic videos (111 videos, $>$ 278,000 annotated objects), and a graphical user interface (GUI) is designed to employ and test the proposed model effectively. TOD-CNN is highly accurate, achieving $85.60\%$ AP$_{50}$ in the task of real-time sperm detection in microscopic videos. To demonstrate the importance of sperm detection technology in sperm quality analysis, we carry out relevant sperm quality evaluation metrics and compare them with the diagnosis results from medical doctors.

Related papers

• Visible and Clear: Finding Tiny Objects in Difference Map [50.54061010335082]
  We introduce a self-reconstruction mechanism in the detection model, and discover the strong correlation between it and the tiny objects. Specifically, we impose a reconstruction head in-between the neck of a detector, constructing a difference map of the reconstructed image and the input, which shows high sensitivity to tiny objects. We further develop a Difference Map Guided Feature Enhancement (DGFE) module to make the tiny feature representation more clear.
  arXiv  Detail & Related papers  (2024-05-18T12:22:26Z)
• ACTIVE: A Deep Model for Sperm and Impurity Detection in Microscopic Videos [17.3840418564686]
  We introduce a deep learning model based on Double Branch Feature Extraction Network (DBFEN) and Cross-conjugate Feature Pyramid Networks (CCFPN) Experiments show that the highest AP50 of the sperm and impurity detection is 91.13% and 59.64%, which lead its competitors by a substantial margin and establish new state-of-the-art results in this problem.
  arXiv  Detail & Related papers  (2023-01-15T02:24:17Z)
• VISEM-Tracking, a human spermatozoa tracking dataset [3.1673957150053713]
  We provide a dataset called VISEM-Tracking with 20 video recordings of 30 seconds (comprising 29,196 frames) of wet sperm preparations. We present baseline sperm detection performances using the YOLOv5 deep learning (DL) model trained on the VISEM-Tracking dataset.
  arXiv  Detail & Related papers  (2022-12-06T09:25:52Z)
• Object Detection in Aerial Images with Uncertainty-Aware Graph Network [61.02591506040606]
  We propose a novel uncertainty-aware object detection framework with a structured-graph, where nodes and edges are denoted by objects. We refer to our model as Uncertainty-Aware Graph network for object DETection (UAGDet)
  arXiv  Detail & Related papers  (2022-08-23T07:29:03Z)
• StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational Autoencoder [48.2010192865749]
  Unsupervised anomaly detection (UAD) can learn a data distribution from an unlabelled dataset of healthy subjects and then be applied to detect out of distribution samples. This research proposes a compact version of the "context-encoding" VAE (ceVAE) model, combined with pre and post-processing steps, creating a UAD pipeline (StRegA) The proposed pipeline achieved a Dice score of 0.642$pm$0.101 while detecting tumours in T2w images of the BraTS dataset and 0.859$pm$0.112 while detecting artificially induced anomalies.
  arXiv  Detail & Related papers  (2022-01-31T14:27:35Z)
• Transfer Learning of Deep Spatiotemporal Networks to Model Arbitrarily Long Videos of Seizures [58.720142291102135]
  Detailed analysis of seizure semiology is critical for management of epilepsy patients. We present GESTURES, a novel architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs) We show that an STCNN trained on a HAR dataset can be used in combination with an RNN to accurately represent arbitrarily long videos of seizures.
  arXiv  Detail & Related papers  (2021-06-22T18:40:31Z)
• Few-Shot Learning for Video Object Detection in a Transfer-Learning Scheme [70.45901040613015]
  We study the new problem of few-shot learning for video object detection. We employ a transfer-learning framework to effectively train the video object detector on a large number of base-class objects and a few video clips of novel-class objects.
  arXiv  Detail & Related papers  (2021-03-26T20:37:55Z)
• Predicting Semen Motility using three-dimensional Convolutional Neural Networks [0.0]
  We propose an improved deep learning based approach using three-dimensional convolutional neural networks to predict sperm motility from microscopic videos of the semen sample. Our models indicate that deep learning based automatic semen analysis may become a valuable and effective tool in fertility and IVF labs.
  arXiv  Detail & Related papers  (2021-01-08T07:38:52Z)
• An Analysis of Deep Object Detectors For Diver Detection [19.14344722263869]
  We produce a dataset of approximately 105,000 annotated images of divers sourced from videos. We train a variety of state-of-the-art deep neural networks for object detection, including SSD with Mobilenet, Faster R-CNN, and YOLO. Based on our results, we recommend Tiny-YOLOv4 for real-time applications on robots.
  arXiv  Detail & Related papers  (2020-11-25T01:50:32Z)
• DeepShadows: Separating Low Surface Brightness Galaxies from Artifacts using Deep Learning [70.80563014913676]
  We investigate the use of convolutional neural networks (CNNs) for the problem of separating low-surface-brightness galaxies from artifacts in survey images. We show that CNNs offer a very promising path in the quest to study the low-surface-brightness universe.
  arXiv  Detail & Related papers  (2020-11-24T22:51:08Z)
• Sperm Detection and Tracking in Phase-Contrast Microscopy Image Sequences using Deep Learning and Modified CSR-DCF [0.0]
  In this article, we use RetinaNet, a deep fully convolutional neural network as the object detector. The average precision of the detection phase is 99.1%, and the F1 score of the tracking method is 96.61%. These results can be a great help in studies investigating sperm behavior and analyzing fertility possibility.
  arXiv  Detail & Related papers  (2020-02-11T00:38:47Z)

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.