We lifted 4 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| Lorna-Liu/ultrasound_vsumm_RL | pwc_unofficial | 4 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| centering | Ran | Lorna-Liu/ultrasound_vsumm_RL/utils/KTS/cpd_auto.py code served (permissive licence) · get_code("492f9a88825ebb1d") |
| estimate_vmax | Ran | Lorna-Liu/ultrasound_vsumm_RL/utils/KTS/cpd_auto.py code served (permissive licence) · get_code("9c790870308b6c5a") |
| knapsack_dp | Ran | Lorna-Liu/ultrasound_vsumm_RL/utils/knapsack.py code served (permissive licence) · get_code("96b2828743f88070") |
| read_json | Ran | Lorna-Liu/ultrasound_vsumm_RL/utils/file_process.py code served (permissive licence) · get_code("7d6e263caea8e963") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Identifying and interpreting fetal standard scan planes during 2D ultrasound mid-pregnancy examinations are highly complex tasks which require years of training. Apart from guiding the probe to the correct location, it can be equally difficult for a non-expert to identify relevant structures within the image. Automatic image processing can provide tools to help experienced as well as inexperienced operators with these tasks. In this paper, we propose a novel method based on convolutional neural networks which can automatically detect 13 fetal standard views in freehand 2D ultrasound data as well as provide a localisation of the fetal structures via a bounding box. An important contribution is that the network learns to localise the target anatomy using weak supervision based on image-level labels only. The network architecture is designed to operate in real-time while providing optimal output for the localisation task. We present results for real-time annotation, retrospective frame retrieval from saved videos, and localisation on a very large and challenging dataset consisting of images and video recordings of full clinical anomaly screenings. We found that the proposed method achieved an average F1-score of 0.798 in a realistic classification experiment modelling real-time detection, and obtained a 90.09% accuracy for retrospective frame retrieval. Moreover, an accuracy of 77.8% was achieved on the localisation task.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("1612.05601")
get_code_for_paper("1612.05601")
have("1612.05601")
Connect an agent — have() is free.