User insights shaping machine learning applied to archives

Archives hold vast amounts of historical and cultural information, but navigating and extracting knowledge can be a daunting task. Machine learning (ML) offers immense potential to unlock these archives, yet its effectiveness hinges on understanding user needs. This paper explores how user insights can shape the development and application of ML in archives. Here “user” refers to editors and publishers who are crucial part of archival sorting and publication in the company. This paper emphasizes the importance of an iterative user centred design process to guide development and ensure user acceptance and empowerment. This approach reveals the distance between user expectations and functional integrity

Object Detection in Heritage Archives using a Human-in-Loop Concept

The use of object detection has become common within the area of computer vision and has been considered essential for a numerous applications. Currently, the field of object detection has undergone significant development and can be broadly classified into two categories: traditional machine learning methods that employ diverse computer vision techniques, and deep learning methods. This paper proposes a methodology that incorporates the human-in-loop feedback concept to enhance the deep learning object detection capabilities of pre-trained models. These Deep Learning models were developed using a custom humanities and social science dataset that was obtained from the British Online Archives collections database

High-Resolution Harmonics Ultrasound Imaging for Non-Invasive Characterization of Wound Healing in a Pre-Clinical Swine Model

<div><p>This work represents the first study employing non-invasive high-resolution harmonic ultrasound imaging to longitudinally characterize skin wound healing. Burn wounds (day 0-42), on the dorsum of a domestic Yorkshire white pig were studied non-invasively using tandem digital planimetry, laser speckle imaging and dual mode (B and Doppler) ultrasound imaging. Wound depth, as measured by B-mode imaging, progressively increased until day 21 and decreased thereafter. Initially, blood flow at the wound edge increased up to day 14 and subsequently regressed to baseline levels by day 21, when the wound was more than 90% closed. Coinciding with regression of blood flow at the wound edge, there was an increase in blood flow in the wound bed. This was observed to regress by day 42. Such changes in wound angiogenesis were corroborated histologically. Gated Doppler imaging quantitated the pulse pressure of the primary feeder artery supplying the wound site. This pulse pressure markedly increased with a bimodal pattern following wounding connecting it to the induction of wound angiogenesis. Finally, ultrasound elastography measured tissue stiffness and visualized growth of new tissue over time. These studies have elegantly captured the physiological sequence of events during the process of wound healing, much of which is anticipated based on certain dynamics in play, to provide the framework for future studies on molecular mechanisms driving these processes. We conclude that the tandem use of non-invasive imaging technologies has the power to provide unprecedented insight into the dynamics of the healing skin tissue.</p></div

Laser speckle perfusion imaging shows dynamic changes in wound-site blood flow over time.

<p>(A) Perfusion was visualized as a two-dimensional color-coded map of blood flow (red = high; blue = low). Perfusion maps were acquired for all time points. A hashed line box representing the original wound size (1”x1”) was drawn on perfusion images to show changes in perfusion and wound size over time. (B and C) Mean perfusion at the wound edge (B) and wound bed (C) from all the time points are shown in the line graph. Data represent mean ± SD. (Scale bar = 1cm). (n = 3 pigs).</p

von Willebrand’s Factor and Collagen IV staining corroborate tissue perfusion imaging observations.

<p>OCT embedded frozen wound biopsies were sectioned (10 μm) and stained using anti-ColIV (green), anti-vWF (red) and DAPI (blue). Shown are representative images of the stained tissue sections from the edge and bed of the wound on days 3, 7, 14 and 42. (Scale bar = 500 μm)</p

Digital image planimetry and ultrasound B-mode imaging helps visualize the progress of wound healing on a real-time basis.

<p>(A) Digital images showing a time course of wound healing starting at day 0 (immediately post-burn) and ending at day 42. Hashed line box of size 1”×1” was drawn on d0 wounds to show the actual wound size. (B) Ultrasound based axial B-mode images from the time course of the study are shown. Included is a baseline image from the normal skin pre-burn (pre). The hashed lines indicated in the images—pre d14, represent the distance of the subcutaneous tissue from the epidermal layer. The lines in images d21- d42 outline the cavitation area visualized by this imaging. (C) Image planimetry data was plotted over the time course of the study.Data are mean ± SD, n = 3 pigs. (Scale bar = 1cm). (D) Wound depth quantification was performed based on the B-mode images for all time points and represented graphically. Pre-burn images were used to measure baseline skin thickness. (E) Using the area and depth measurements, wound volume was calculated and represented graphically. Data presented as mean ± SD. (Scale bar = 1 cm).</p

Tissue elastography enabled visualization of the existing and nascent tissue color-coded for their biomechanical properties.

<p>Skin hardness and elasticity of the wound was mapped over time as the wound heals. (A) Shown are color maps of the elasticity of the wound tissue over time. Asterisks (d21—d42 images) mark the presence of cavitation area. (n = 3 pigs). (B) Shown are representative images of formalin-fixed paraffin-embedded biopsy tissue sections (5 μm) of normal and wounded skin (Day 42) that were stained using Massons trichrome method. Staining results in blue-black nuclei, blue collagen and light red/pink cytoplasm. Epidermal cells appear red. Scale bar = 4mm. (C) Quantification of the thickness of the scar from day 35 and 42 are shown graphically. (D-F) The strength of the healing burn wounds were assessed using a TestResources mechanical tester. All skin samples were tested to failure at a strain rate of 0.05 in/sec. Load versus position for each group (normal skin, d14 and d42) is plotted; (n = 3 pigs); (Scale bar = 1cm).</p