A survey on cancer detection via convolutional neural networks: Current challenges and future directions
Cancer is a condition in which abnormal cells uncontrollably split and damage the body
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
tissues. Hence, detecting cancer at an early stage is highly essential. Currently, medical …
Polyp-pvt: Polyp segmentation with pyramid vision transformers
Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …
when exchanging information between the encoder and decoder: 1) taking into account the …
[HTML][HTML] Machine learning based small bowel video capsule endoscopy analysis: Challenges and opportunities
Video capsule endoscopy (VCE) is a revolutionary technology for the early diagnosis of
gastric disorders. However, owing to the high redundancy and subtle manifestation of …
gastric disorders. However, owing to the high redundancy and subtle manifestation of …
SinGAN-Seg: Synthetic training data generation for medical image segmentation
Analyzing medical data to find abnormalities is a time-consuming and costly task,
particularly for rare abnormalities, requiring tremendous efforts from medical experts …
particularly for rare abnormalities, requiring tremendous efforts from medical experts …
TMF-Net: A transformer-based multiscale fusion network for surgical instrument segmentation from endoscopic images
Automatic surgical instrument segmentation is a necessary step for the steady operation of
surgical robots, and the segmentation accuracy directly affects the surgical effect …
surgical robots, and the segmentation accuracy directly affects the surgical effect …
[HTML][HTML] Meta-learning with implicit gradients in a few-shot setting for medical image segmentation
Widely used traditional supervised deep learning methods require a large number of
training samples but often fail to generalize on unseen datasets. Therefore, a more general …
training samples but often fail to generalize on unseen datasets. Therefore, a more general …
Li-SegPNet: Encoder-decoder mode lightweight segmentation network for colorectal polyps analysis
Objective: One of the fundamental and crucial tasks for the automated diagnosis of
colorectal cancer is the segmentation of the acute gastrointestinal lesions, most commonly …
colorectal cancer is the segmentation of the acute gastrointestinal lesions, most commonly …
Irv2-net: A deep learning framework for enhanced polyp segmentation performance integrating inceptionresnetv2 and unet architecture with test time augmentation …
Colorectal polyps in the colon or rectum are precancerous growths that can lead to a more
severe disease called colorectal cancer. Accurate segmentation of polyps using medical …
severe disease called colorectal cancer. Accurate segmentation of polyps using medical …
DRR-Net: A dense-connected residual recurrent convolutional network for surgical instrument segmentation from endoscopic images
The precise segmentation of surgical instruments is the key link for the stable and
reasonable operation of surgical robots. However, accurate surgical instrument …
reasonable operation of surgical robots. However, accurate surgical instrument …
An attention-guided network for surgical instrument segmentation from endoscopic images
Accurate surgical instrument segmentation can provide the precise location and pose
information to the surgeons, assisting the surgeon to accurately judge the follow-up …
information to the surgeons, assisting the surgeon to accurately judge the follow-up …