Omkar2118/datasets huggingface dataset:
APRICOT-Mask

We present the APRICOT-Mask dataset, which augments the APRICOT dataset with pixel-level annotations of adversarial patches. We hope APRICOT-Mask along with the APRICOT dataset can facilitate the research in building defenses against physical patch attacks, especially patch detection and removal techniques.

Introducing paper: Segment and Complete: Defending Object Detectors against Adversarial Patch Attacks with Robust Patch Detection
Tasksource

Huggingface Datasets is a great library, but it lacks standardization, and datasets require preprocessing work to be used interchangeably. tasksource automates this and facilitates reproducible multi-task learning scaling.

Each dataset is standardized to either MultipleChoice, Classification, or TokenClassification dataset with identical fields. We do not support generation tasks as they are addressed by promptsource. All implemented preprocessings are in tasks.py or tasks.md. A preprocessing is a function that accepts a dataset and returns the standardized dataset. Preprocessing code is concise and human-readable.

Introducing paper: tasksource: A Dataset Harmonization Framework for Streamlined NLP Multi-Task Learning and Evaluation
HeiChole Benchmark
Full name: Surgical Workflow and Skill Analysis Challenge (HeiChole Benchmark)

Analyzing the surgical workflow is a prerequisite for many applications in computer assisted surgery (CAS), such as context-aware visualization of navigation information, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery. Since laparoscopic surgeries are performed using an endoscopic camera, a video stream is always available during surgery, making it the obvious choice as input sensor data for workflow analysis. Moreover, this offers the opportunity for structured assessment of surgical skill for safety, teaching and quality management.

The sub-challenge “Surgical Workflow and Skill Analysis” focuses on the online workflow analysis of laparoscopic surgeries. Participants are challenged to segment laparoscopic surgeries for gallbladder removal (cholecystectomy) into surgical phases, to recognize instrument presence and surgical actions as well as to classify surgical skill based on video data. Participants are encouraged (but not required!) to submit different results for phase segmentation, action recognition, instrument presence and skill classification . This novel kind of challenge investigates the current state-of-the-art results on surgical workflow analysis and skill assessment on one comprehensive dataset.

Introducing paper: Comparative Validation of Machine Learning Algorithms for Surgical Workflow and Skill Analysis with the HeiChole Benchmark
Endoscapes
Full name: Endoscapes - Semantic Segmentation

Cholecystectomy is a very common abdominal surgical procedure almost ubiquitously performed with a laparoscopic approach, hence guided by an endoscopic video. Deep learning
models for LC video analysis have been developed with the aim of assisting surgeons during interventions, improving staff awareness and readiness, and facilitating postoperative documentation and research. . However, datasets and models for video semantic segmentation of LC are lacking. Recognizing fine-grained hepatocystic anatomy through semantic segmentation could help surgeons better assess the critical view of safety (CVS), a universally recommended technique consisting in well exposing anatomical landmarks to prevent bile duct injuries. Additionally, segmentation masks of hepatocystic structures could be leveraged by deep learning models for automatic assessment of CVS  and surgical action recognition to improve their performance. We believe that generating a dataset for video semantic segmentation of hepatocystic anatomy will promote surgical data science research and accelerate the development of applications for surgical safety. To generate a representative dataset, consecutive endoscopic videos of LC performed at Nouvel Hopital Civil (Strasbourg, France) were collected.  Non-endoscopic, i.e., out-of-body, video frames were blackedout to comply with European data protection regulations. A frame every 30 seconds was sampled from the portion of the endoscopic video showing the hepatocystic anatomy being dissected, the most critical phase of the surgical procedure, and when surgeons should achieve the CVS. Such unselected and regularly spaced video frames were manually annotated with pixel-wise semantic annotations of anatomical and surgical instances, such as the cystic artery and the dissection. Overall, 1933 regularly spaced video frames from 201 LC videos were annotated with segmentation mask for 29 classes of the hepatocystic triangle, respectively.
performed in double by specifically trained computer scientists
and surgeons.

Introducing paper: Temporally Constrained Neural Networks (TCNN): A framework for semi-supervised video semantic segmentation